JP7620093B2 - Inspection guide device and computer-aided inspection guide method - Google Patents

Description

The present invention relates to an examination guidance device and a computer-based examination guidance method that can provide appropriate guidance to subjects (who are to be distinguished from subjects who are participating in research, etc., and are referred to as subjects as those who undergo the examination) who undergo examinations involving medical procedures such as endoscopic examinations or clinical examinations, until they actually undergo the examination.

Medical facilities are equipped with multiple endoscopes and other examination devices, and an appropriate endoscope is prepared according to the examination items of the patient or examinee, and this endoscope is used for the examination. In this case, a schedule must be set to use the endoscope in good condition, taking into consideration the cleaning condition of the endoscope, as well as its wear and tear and deterioration. Therefore, Patent Document 1 proposes an endoscopic examination work support system that appropriately schedules endoscopic examination work.

JP 2017-113082 A

The endoscopic examination support system of the aforementioned Patent Document 1 describes a technology for appropriately scheduling endoscopic examination work in a medical facility, but does not describe support such as advice for subjects undergoing examinations in a medical facility to undergo appropriate examinations. In actual medical settings, there are various things that subjects must do when undergoing endoscopic examinations, such as taking a laxative and performing digestive tract irrigation before undergoing endoscopic examinations, and the necessary treatments, the time required to take treatments such as laxatives, and the examination time, etc. vary depending on the health condition and individual differences of the subjects. Therefore, unless appropriate advice is given including the condition of the subjects, such as taking treatment drugs including laxatives before the examination, physical condition management during the examination, and the health condition and individual differences of the subjects, it is difficult for the subjects to feel fully at ease when undergoing the examination.

The present invention has been made in consideration of the above circumstances, and aims to provide an examination guidance device and a computer-based examination guidance method that enable a patient to receive the advice necessary to properly undergo examinations involving medical procedures, such as endoscopic examinations, and clinical examinations.

In order to achieve the above-mentioned object, a computer-based examination guide method according to a first invention is a computer-based examination guide method including the steps of automatically estimating, by a computer , the degree of progress of a process relating to preparation for an endoscopic examination of an endoscopic examination subject based on temporal changes in condition information of the endoscopic examination subject, and automatically generating, by a computer , information relating to the end timing of the preparation process based on the estimated degree of progress, wherein the temporal changes in the condition information are temporal changes in an excretion image, and based on the temporal changes in the excretion image, a temporal change in the degree of cleansing of the intestine in the future from the time the excretion image was captured is estimated by a computer , and the timing at which the estimated temporal change in cleansing degree reaches a certain level is determined by the computer as the end timing of the intestinal cleansing state.

A computer-based examination guidance method according to a second aspect of the present invention is a computer-based examination guidance method including the steps of automatically estimating, by a computer , the degree of progress of a process relating to preparation for an endoscopic examination of an endoscopic examination patient based on temporal changes in condition information of the endoscopic examination patient, and automatically generating, by a computer , information relating to the end timing of the preparation process based on the estimated degree of progress, wherein the degree of progress of the preparation process is the degree of progress of the intestinal cleansing state of the endoscopic examination patient, and the end timing of the preparation process is determined to be the timing predicted by the computer when the intestinal cleansing state reaches a certain degree, and an excretion image captured of the state in which the endoscopic examination patient excretes with a plurality of the excretion images is compared by a computer with an excretion progress model and features to estimate temporal changes in the degree of cleansing, and the end timing of the intestinal cleansing state is estimated by the computer .

The computer-based examination guidance method according to the third invention is the same as that of the second invention, in which the time from the start of intestinal cleansing of the patient undergoing endoscopic examination is further taken into account, and the change in cleansing level over time is estimated by a computer .
The computer-based examination guidance method according to the fourth invention is the same as that of the second invention, in which the amount of intestinal cleansing agent taken by the endoscopy examination subject is estimated by a computer , and this estimated amount of intestinal cleansing agent is further taken into account to estimate the change in cleansing level over time.

The computer-based examination guidance method according to the fifth invention is the same as that of the second invention, in which an AI model trained using excretion images of the patient excreting, and a plurality of excretion images of the patient excreting in the state where the patient is undergoing an endoscopic examination, are used to estimate the end timing of the intestinal cleansing state by a computer .
The computer-based examination guidance method of the sixth invention is the same as that of the second invention, except that in addition to the excretion images taken of the excretion state, an AI model is trained using at least one of the time until completion of cleaning and the amount of cleaning agent, and an excretion image taken of the state in which the subject excretes is input, and the computer estimates the timing when the intestinal cleansing state will end.

The seventh invention relates to a computer-based examination guidance method, which is the same as the second invention, in that an AI model is trained using a plurality of learning data sets in which images of the excretion state are annotated with the degree of cleansing, and an image of the patient undergoing an endoscopic examination excreting is input to the AI model, which estimates the degree of cleansing by computer and estimates the timing when the intestinal cleansing state will end.
The computer-based inspection guide method according to the eighth invention is the same as that of the second invention described above, in which a plurality of images of the excretion state of the subject are input to an AI model trained using a plurality of learning data sets in which a plurality of images of the excretion state are annotated with the cleaning time or the amount of detergent ingested until the subject reaches a state where the excretion can be inspected, and the computer estimates the timing when the cleaning will end.

The examination guide device of the ninth invention has a status acquisition unit that acquires status information of an endoscopic examination patient , a progress estimation unit that estimates the progress of the examination preparation of the endoscopic examination patient based on temporal changes in the status information, and an advice generation unit that displays advice regarding the examination preparation based on the progress, wherein the progress estimation unit estimates the progress of digestive tract cleansing and the time required to reach a certain level of cleansing or the number of times and amount of cleansing agent to be ingested, and the advice generation unit generates information based on the estimation results of the progress estimation unit.

According to the present invention, it is possible to provide an examination guide device and a computer-based examination guide method that enable a patient to receive necessary advice in order to appropriately undergo examinations involving medical procedures, such as endoscopic examinations, or clinical examinations.

1 is a block diagram showing a configuration of an endoscopic examination support system according to a first embodiment of the present invention. 5 is a flowchart showing a main operation of a service server of the endoscopic examination support system according to the first embodiment of the present invention. 5 is a flowchart showing the operation of displaying an examination time guide in the service server of the endoscopic examination support system according to the first embodiment of the present invention. 13 is a flowchart showing a modified example of the operation of displaying a guide on examination time in the service server of the endoscopic examination support system according to the first embodiment of the present invention. 4 is a graph showing changes in cleanliness in the endoscopic examination support system according to the first embodiment of the present invention. 1A to 1C are diagrams showing a reference image sequence of defecation in the endoscopic examination support system according to the first embodiment of the present invention. 1 is a diagram for explaining a method for calculating the cleanliness level by AI in the endoscopic examination support system according to the first embodiment of the present invention. FIG. 1 is a graph showing a cleanliness curve when the cleanliness is predicted by an algorithm in the endoscopic examination support system according to the first embodiment of the present invention. FIG. 11 is a diagram showing an algorithm for predicting cleanliness using AI in the endoscopic examination support system according to the first embodiment of the present invention. FIG. 13 is a diagram showing an alarm display screen when a subject starts to drink a cleaning agent in the endoscopic examination support system according to the first embodiment of the present invention. FIG. 13 is a diagram showing an advice screen for a subject when drinking a cleaning agent in the endoscopic examination support system according to the first embodiment of the present invention. FIG. 13 is a diagram showing an advice screen displayed from when a subject leaves his/her home until the examination is completed at a medical facility in the endoscopic examination support system according to the first embodiment of the present invention. FIG. 11 is a block diagram showing the configuration of an endoscopic examination support system according to a second embodiment of the present invention. 10 is a flowchart showing a main operation of a service server of the endoscopic examination support system according to the second embodiment of the present invention. FIG. 13 is a diagram showing an advice screen displayed from when a subject arrives at a medical facility to when the examination is completed, in the endoscopic examination support system according to the second embodiment of the present invention.

Below, as an embodiment of the present invention, an example in which the present invention is applied to an endoscopic examination support system will be described. In a colonoscopic examination, in order to examine the large intestine, a cleansing agent for the intestine is drunk and the large intestine is cleaned before the examination, so in this embodiment, the case in which the intestine is cleansed will be mainly described. Of course, the present invention is not limited to endoscopic examination, and can also be applied to other clinical examinations and examinations involving medical procedures. First, the endoscopic examination support system according to the first embodiment will be described with reference to Figures 1 to 9C. Figure 1 is a block diagram showing the overall configuration of the endoscopic examination support system according to the first embodiment. This endoscopic examination support system is composed of a service server 10, a user terminal 20, and in-hospital systems 30 and 34.

The service server 10 can be connected to a user terminal 20 used by users, etc., and in-hospital systems 30, 34 used by medical staff, etc. in medical facilities and for acquiring information in medical facilities, through a communication network such as the Internet, and can provide various services to users, etc. The service server 10 has a processing device such as a CPU (Central Processing Unit), a memory storing a program, and other peripheral circuits, and has a control unit 11, a communication unit 12, a schedule management unit 13, a cleanliness determination unit 14, a time prediction unit 15, an instruction transmission unit 16, and an examination result recording unit 17. The schedule management unit 13, the cleanliness determination unit 14, the time prediction unit 15, the instruction transmission unit 16, and the examination result recording unit 17 may be realized by a hardware circuit, etc., or may be realized by the control unit 11 executing a program stored in the memory. In this case, one or more processors constituting the control unit 11 perform the functions of the schedule management unit 13, the cleanliness determination unit 14, the time prediction unit 15, and the instruction transmission unit 16.

The control unit 11 controls the entire service server 10. The control unit 11 is composed of one or more processors having a processing device such as a CPU, a memory storing a program, etc., and can control each part in the service server 10 by executing a program. The control unit 11 also functions as a usage status acquisition unit that acquires the usage status of the medical institution where the patient undergoes an endoscopic examination from the waiting room information acquisition unit 37, the examination room information acquisition unit 38, and the recovery room information acquisition unit 39 of the in-hospital system 34 (see S15, S17, etc. in Figure 2). The usage status is the usage status (crowding status) of the space in the medical institution where each action related to at least one of waiting, pre-treatment, examination, recovery, and payment is performed.

The communication unit 12 has a communication circuit provided in the peripheral circuit of the service server 10, and can communicate with each communication unit in the user terminal 20, the hospital system 30, and 34. For example, when the instruction issuing unit 16 creates advice, it transmits the advice to the user terminal 20. In addition, the subject's request can be input through the communication unit 12. For example, if the subject wishes to have the examination using a painkiller or a sedative, this is input to the user terminal 20 and transmitted to the service server 10 through the communication unit 12. In addition, the subject can input requests regarding transportation and the like when moving from home to the medical facility. When the service server 10 functions as an examination guide device, the communication unit 12 functions as a patient request input unit that inputs the requests of the endoscopy examination patient.

The schedule management unit 13 performs various time management when the user (subject) undergoes an endoscopic examination. For example, it manages the timing at which the user starts taking the intestinal cleansing agent when undergoing the examination (see S3 in FIG. 2), the departure time when the user leaves for a medical facility such as a hospital to undergo the examination (see S11 and S13 in FIG. 2), and the predicted time of all steps of the examination (S19 in FIG. 2). In addition, the schedule management unit 13 performs schedule management taking into account the examination time predicted by the time prediction unit 15, etc. (see S15, S17, and S19 in FIG. 2).

The cleanliness assessment unit 14 assesses the degree of cleansing of the subject's intestinal tract based on images of the subject's defecation, etc. The subject drinks the cleansing agent multiple times, and each time the subject defecates, the intestinal tract is gradually cleansed. The cleanliness assessment unit 14 assesses this degree of cleanliness. The assessment of the cleanliness level based on defecation images will be described later with reference to Figures 4 to 8. When the service server 10 functions as an examination guide device, the cleanliness assessment unit 14 functions as a status acquisition unit that acquires status information of the patient undergoing an endoscopic examination (see, for example, S5 in Figure 2, Figures 3A and 3B, S14a in Figure 11, etc.). The status acquisition unit acquires information related to the patient's excretion.

The time prediction unit 15 predicts the time when the subject will undergo the test. For example, when the subject takes a cleaning agent at home to cleanse the inside of the intestine, the time when the cleaning will end is predicted based on the time change of the result of judging the cleansing degree based on the image of the subject's defecation (see, for example, Figures 4 and 8). In addition, the time prediction unit 15 performs time prediction based on the time change of the judgment result of the intestinal cleansing degree performed by the cleaning judgment unit 14 (for example, the time change of the excretion image, see the reference image sequence in Figure 5). In addition, the timing when it is predicted that the intestinal cleansing degree will reach a certain level is set as the end timing (see, for example, S49 in Figures 3A and 3B, and the testable level Lev in Figure 4). Based on this prediction result, the amount of cleaning agent and the number of times that the subject will drink until the cleansing is completed are also predicted.

In addition to cleaning, the time prediction unit 15 may also predict the time when preparation for the test will end, the time when the test will start, the time when the test will be completed, etc. For example, the time to start drinking and the time to leave may be predicted by working backwards from the time required for the test (see, for example, S3 and S11 in FIG. 2), and in a medical facility, the time required for the entire process of the test may be predicted taking into account the time required for the test, recovery, etc., the time required to hear the test results after the test is completed, the time required to wait for payment, etc. (see, for example, S19 in FIG. 2).

When the service server 10 functions as an examination guide device, the time prediction unit 15 functions as a progress estimation unit that estimates the progress of the examinee's examination preparation based on the temporal change in the status information (see, for example, S49 in Figures 3A and 3B). The examination preparation is preparation related to digestive tract cleansing. In this embodiment, this corresponds to the examinee drinking a cleansing agent to cleanse the intestines. The progress estimation unit estimates the examinee's progress related to digestive tract cleansing as the progress. The progress estimation unit estimates the progress of digestive tract cleansing and the time required to reach a certain degree of cleansing, or the number and amount of times the cleansing agent will be ingested (see, for example, S49 in Figures 3A and 3B).

The time prediction unit 15 functions as a time estimation unit that estimates the time related to the endoscopic examination based on the status information and the usage situation (for example, see S19 in Figure 2). The time estimation unit predicts the start or end time of each action related to at least one of waiting, pre-treatment, examination, recovery, and payment at the medical institution.

The instruction sending unit 16 creates advice for the subject and sends it from the service server 10 to the user terminal 20, etc. through the communication unit 12. The advice includes, for example, the time when the cleaning will end, the amount and number of times of the remaining cleaning agent, etc. based on the subject's defecation (see, for example, S49 in Figures 3A and 3B). In addition to this, there is also advice on the departure time and cleaning time (see Figures 9A to 9C). When the service server 10 functions as an examination guide device, the instruction sending unit 16 functions as an advice display unit that displays advice on examination preparation based on the progress status (see S49 in Figures 3A and 3B, Figures 9A to 9C, etc.). The advice display unit displays information based on the estimation result of the progress estimation unit (cleaning level, number and amount of cleaning agent intake, expected time of completion of cleaning, etc.). The advice display unit displays advice taking into account the requests of the endoscopy examination patient.

When the service server 10 functions as an examination guide device, the instruction transmission unit 16 functions as an advice display unit that displays advice related to the endoscopic examination based on estimation. The advice display unit displays advice related to the start of travel to the medical institution, the start or end of each action, and the completion of all actions at the medical institution. The instruction transmission unit 16 functions as an advice generation unit (see, for example, S49 in Figures 3A and 3B) that displays advice related to examination preparation based on the progress status. The advice generation unit generates information based on the estimation result of the progress estimation unit.

The examination result recording unit 17 records the examination results in the recording unit when the subject completes an examination such as an endoscopic examination at the hospital.

In this embodiment, the in-hospital system 30 and the in-hospital system 34 are provided in the same hospital, and multiple in-hospital systems are also provided in other hospitals. One of the in-hospital systems 30 and 34 provided in the same hospital is a system that is connected to mobile terminals and PCs (personal computers) used by doctors, nurses, and staff in the administrative department and dispensing department, etc., and exchanges various information, and the other is a system that is connected to equipment for acquiring information in patient waiting rooms, examination rooms, recovery rooms, etc., such as monitoring devices having imaging units, and mobile terminals and PCs used by staff in medical facilities, and exchanges various information. If there are three or more systems in the same hospital, it is of course possible to provide three or more in-hospital systems, or to combine them into one.

The control units 31, 35 in the in-hospital systems 30, 34 control the entire in-hospital systems 30, 34. The control units 31, 35 have a processing device such as a CPU, a memory that stores a program, etc., and can execute the program and control each unit in each in-hospital system. The control units 31, 35 may also be configured so that the in-hospital systems 30, 34 in the same hospital operate in cooperation with each other.

The communication units 32, 36 have communication circuits and can communicate with the service server 10 and each communication unit in other in-hospital systems 30, 34.

The schedule management unit 33 manages the schedules of users (doctors, nurses, pharmacists, laboratory technicians, office staff, etc.) of mobile terminals and PCs in the in-hospital system 30. This schedule management is performed in cooperation with the schedule management unit 13 in the service server 10, and is linked to the subject's examination schedule (including before the examination, at the time of the examination, and after the examination).

The waiting room is a room where subjects wait until undergoing an endoscopic examination. The waiting room information acquisition unit 37 has an imaging unit such as a camera and an audio collection unit such as a microphone arranged in the waiting room, and acquires various information about the waiting room. Information about the waiting room is acquired based on the information collected by the imaging unit and audio collection unit. If the waiting room information acquisition unit 37 can also directly communicate with the user terminal 20, it can detect that a subject having a user terminal 20 is in the waiting room. Furthermore, it can acquire various information such as what kind of examination the subject has come to receive, what the schedule is, and the subject's profile. In addition, a nurse or the like may directly input information into the in-hospital system 34 as text or images from an in-hospital terminal.

The examination room is a room where the subject undergoes an endoscopic examination. The examination room information acquisition unit 38 has an imaging unit such as a camera and an audio collection unit such as a microphone arranged in the examination room, and acquires various information related to the endoscopic examination that the subject undergoes in the examination room. Information on the examination room is acquired based on the information collected by the imaging unit and audio collection unit. In addition, if the examination room information acquisition unit 38 can directly communicate with the user terminal 20, it can detect that the subject having the user terminal 20 is in the examination room. Furthermore, it is also possible to acquire information such as the progress of the subject's examination and the examination results. In addition, a nurse or the like may directly input information to the in-hospital system 34, or input text or images from an in-hospital terminal. In many cases, multiple examination rooms are installed. Therefore, it is preferable that the examination room information acquisition unit 38 acquires the information for each examination room.

The recovery room is a room where the subject rests after the endoscopy is completed until the effects of the sedative or other medication wear off and the subject returns to a normal state. The recovery room information acquisition unit 39 has an imaging unit such as a camera and an audio collection unit such as a microphone arranged in the recovery room, and acquires various information regarding the subject's condition in the recovery room. Information in the recovery room is acquired based on the information collected by the imaging unit and audio collection unit. In addition, if the recovery room information acquisition unit 39 can directly communicate with the user terminal 20, it can detect that the subject having the user terminal 20 is in the recovery room. Furthermore, information regarding the subject's resting state can be acquired. In addition, a nurse or the like may directly input information into the in-hospital system 34 as text or images from an in-hospital terminal.

The user terminal 20 may be a PC used by the subject, but in this embodiment, a mobile terminal such as a smartphone is assumed for explanation. If it is a mobile terminal, it is easy to collect information on lifestyle habits and to photograph the subject's defecation state because the subject carries it. The user terminal 20 has a processing device such as a CPU (Central Processing Unit), a memory that stores a program, and other peripheral circuits, and the user terminal 20 is provided with a control unit 21, a communication unit 22, an imaging unit 23, and a UI (User Interface) unit 24. Each unit in the user terminal 20 may be realized by a hardware circuit, or some functions may be realized by the control unit 21 executing a program stored in the memory.

The control unit 21 controls the entire user terminal 20. The control unit 21 is composed of one or more processors having a processing device such as a CPU, a memory storing a program, etc., and can execute programs and control each part in the user terminal 20. In this embodiment, schedule management for endoscopic examinations, determination of the cleanliness of stool, time prediction, and issuance of advice to the subject are performed by the schedule management unit 13, cleanliness determination unit 14, time prediction unit 15, and instruction transmission unit 16 in the service server 10.

However, the control unit 21 may execute these functions that are performed within the service server 10. In that case, an inference engine may be provided within the control unit 21 to perform inferences regarding the degree of cleansing, etc. In this case, the user terminal 20 functions as an examination guide device, and the control unit 21 functions as a progress estimation unit that estimates the progress of the examinee's examination preparation based on temporal changes in the status information. The progress estimation unit estimates the progress of the digestive tract cleansing and the time required to reach a certain degree of cleansing, or the number and amount of times the cleansing agent will be ingested (see, for example, S49 in Figures 3A and 3B).

When the user terminal 20 functions as an examination guide device, the control unit 21 functions as a usage status acquisition unit that acquires usage status at the medical institution where the examinee undergoes an endoscopic examination. When the user terminal 20 functions as an examination guide device, the control unit 21 functions as a time estimation unit that estimates the time related to the endoscopic examination based on the status information and the usage status.

The communication unit 22 has a communication circuit provided in the peripheral circuit of the user terminal 20, and can communicate with the communication unit 12 in the service server 10. Through the communication unit 22, various information can be exchanged with the service server 10, such as schedule management, cleaning degree judgment, time prediction, and advice to the subject.

The photographing unit 23 has an image sensor, a photographing lens, an image sensor, an image sensor circuit, etc., and converts the object into image data and outputs it. In this embodiment, the photographing unit 23 is used to acquire images of the subject's defecation. When the mobile terminal 20 functions as an examination guide device, the photographing unit 23 functions as a status acquisition unit that acquires status information of the person undergoing an endoscopic examination (see, for example, S39 in Figures 3A and 3B). The status acquisition unit acquires information related to the subject's excretion.

The UI unit 24 is a user interface for inputting and outputting information to the user terminal 20. The UI unit 24 has a visual (including auditory, etc.) display unit for conveying information to the subject, and an input unit (including, for example, a text input unit, a voice input unit, etc.) for the subject to input information to the user terminal 20. This input unit can also be used to input requests from the subject undergoing an endoscopic examination, such as whether or not the subject wishes to undergo an examination using a sedative. In addition, the subject may be able to input requests regarding transportation, etc., when traveling from their home to the medical facility.

When the user terminal 20 functions as an examination guide device, the UI unit 24 functions as an advice display unit that displays advice regarding examination preparation based on the progress status. The advice display unit displays information based on the estimation results of the progress estimation unit (cleanliness, number of times and amount of detergent intake, expected time of completion of cleaning, etc.). When the user terminal 20 functions as an examination guide device, the UI unit 24 functions as a patient request input unit that inputs the requests of the patient undergoing the endoscopic examination. The advice display unit displays advice taking into account the requests of the patient undergoing the endoscopic examination. When the user terminal 20 functions as an examination guide device, the UI unit 24 functions as an advice display unit that displays advice regarding the endoscopic examination based on the estimation.

By constructing such an endoscopic examination support system, the user (subject) can undergo endoscopic examinations with peace of mind. For example, during an endoscopic examination, the subject may have many questions, such as whether or not further doses are necessary after starting to take the cleaning agent, what time to go to the hospital, what time the examination will start, and what time the examination will end. In fact, some people finish cleaning quickly after taking the cleaning agent, while others take a long time to clean. In such cases, the subject can undergo the examination with peace of mind by using the "peace of mind app" installed in the user terminal 20 (an application executed in the user terminal 20 in parallel with the main flow shown in FIG. 2 described later). In other words, by using this peace of mind app, the subject can receive the necessary advice for undergoing the examination.

Next, the main operation of the application software for the user terminal 20 and the service server 10 to cooperate with each other to provide endoscopic examination support to the subject (user) will be described using the flowchart shown in FIG. 2. The main operation of this application is realized by the control unit 11 in the service server 10 cooperating with the control unit 21 in the user terminal 20 to control each unit in the service server 10, and further cooperating with the in-hospital systems 20 and 35. The flow shown in FIG. 2 is described on the premise that when a safety application for examination assistance is installed in the user terminal 20, the service server 10 takes the lead in providing advice to the user terminal 20 for the subject. In this case, the service server 10 functions as an examination guide device. However, it is of course possible for the user terminal 20 to take the lead in providing advice to the subject with the support of the service server 10. In this case, the user terminal 20 functions as an examination guide device.

When the main operation shown in FIG. 2 starts, first, the estimated examination start time is obtained (S1). Here, the control unit 11 queries the schedule management unit 13 to obtain information regarding the appointment date and time of the subject's endoscopic examination at the medical facility, and the estimated examination start time is known based on this appointment date and time. If there is no appointment at the medical facility, a reservation is made and the estimated examination start time is also obtained. This estimated examination start time is obtained when the subject starts the peace of mind app on the user terminal 20 and notifies the service server 10 that the examination will be performed. Note that when the application software in FIG. 2 is executed independently on the user terminal 20, in this step the user terminal 20 queries the schedule management unit 13 in the service server 10 about the estimated examination start time and obtains the estimated examination start time.

Next, a start-drinking alarm is issued (S3). Here, the control unit 11 determines whether it is time to start taking the detergent based on the expected test start time acquired in step S1, and when it is time to start taking it, it instructs the user terminal 20 from the instruction transmission unit 16 to display an alarm to start taking the detergent on the UI unit 24. The detergent is taken multiple times, and the start-drinking time is determined taking into consideration the standard time it takes to reach a testable cleanliness level. However, since the time it takes to reach a testable cleanliness level differs depending on the subject, a little leeway may be provided from the standard time, and if there is a past record, the information may be taken into consideration when determining the time (see Figures 4, 8, etc.). An example of the display of this start-drinking alarm will be described later with reference to Figure 9A. Note that, when the application software in Figure 2 is executed proactively on the user terminal 20, in this step, the control unit 21 of the user terminal 20 notifies the subject of the start-drinking alarm when it reaches the start-drinking time acquired from the schedule management unit 13.

Next, the cleanliness is checked (S5). Here, the control unit 11 instructs the user terminal 20 through the instruction transmission unit 16 to acquire an image of the subject's defecation by the photographing unit 23. When the subject photographs the defecation image, it is transmitted to the service server 10 through the communication unit 22. The cleanliness determination unit 14 in the service server 10 determines the cleanliness based on the defecation image of the subject, etc., and determines whether or not a sufficient cleanliness level has been reached. By determining the cleanliness, it is possible to predict when the subject will complete the cleanliness (see Figures 4, 8, etc.). The operation of this cleanliness check will be described later with reference to Figures 3A and 3B. Also, during the cleansing, for example, an advice display as shown in Figure 9B is displayed on the UI unit 24 of the user terminal 20. This display will be described later. Note that, when the application software in Figure 2 is executed proactively on the user terminal 20, in this step, the defecation image of the subject is transmitted to the service server 10 and a determination of the cleanliness is requested.

When a sufficient cleansing level is reached, the current location is then obtained (S7). In this embodiment, it is assumed that the subject takes a cleansing agent at home or the like (a place other than a medical facility) to perform intestinal cleansing. Therefore, after intestinal cleansing is completed, the subject moves to a medical facility such as a hospital. In this step, in calculating the time required to move to the medical facility, the control unit 21 first instructs the user terminal 20 to transmit the current location. Information regarding the current location is obtained by a position detection unit such as GPS in the user terminal 20. For this reason, the user terminal 20 has a position detection unit such as GPS. Note that, when the application software in FIG. 2 is executed independently on the user terminal 20, in this step, information regarding the current location is obtained by the position detection unit in the user terminal 20.

After acquiring the current location information, the next step is to acquire the estimated examination start time (S9). In step S1, the estimated examination start time is also acquired, but considering that there may be subsequent changes in the situation, the control unit 11 again inquires of the schedule management unit 33 in the in-hospital system 30 about the estimated examination start time. That is, in the medical facility, the examination may not proceed as planned, or may proceed earlier than planned, and the examination start time is predicted taking into account the progress of the examination at the medical facility. Note that, in the case where the application software in FIG. 2 is executed independently on the user terminal 20, in this step, the control unit 21 acquires the estimated examination start time again through the service server 10 and the in-hospital system 30. The control unit 21 may collect necessary information from the service server 10 and predict the examination start time.

After acquiring the estimated examination start time, it is next determined whether it is the departure time (S11). Here, the control unit 11 calculates the travel start time to the medical facility based on the estimated examination start time acquired in step S9 and the current location information acquired in step S7, and determines whether the current time has reached the calculated travel start time (departure time). The travel start time is calculated taking into account the travel time from the current location to the medical facility. If the subject has a specified means of transportation, the travel time for this means of transportation may be calculated, and multiple means of transportation may be selected. Furthermore, the travel time may be calculated taking into account the traffic congestion situation on the day (for example, road congestion, train operation status). The travel time calculation may be linked to another service that provides guidance on travel routes and means of transportation.

If the result of the determination in step S11 is that it is not time to depart, the process returns to step S7 and the above-mentioned operations are repeated. The progress of the examination at the medical facility and the situation regarding transportation may change, and these changes are taken into consideration when predicting the departure time. Note that, when the application software in FIG. 2 is executed independently on the user terminal 20, in this step the control unit 21 queries the schedule management unit 13 in the service server 10 about the departure time and determines whether the current time is the departure time or not. The control unit 21 may collect necessary information from the service server 10 and the in-hospital systems 30, 34 and determine whether it is time to depart.

If the result of the judgment in step S11 is that it is time to leave, a departure alarm is notified (S13). When the departure time arrives, the instruction transmission unit 16 in the service server 10 instructs the user terminal 20 to display a departure alarm on the UI unit 24. In this case, the instruction transmission unit 16 may notify the user terminal 20 of the departure time in advance, and when the user terminal 20 detects that it is time to leave, a departure alarm may be notified. The departure alarm may be made visually or audibly. In addition, recommended means of transportation and travel routes may be displayed in accordance with the departure alarm. For example, in the case of traveling by train immediately after the completion of intestinal cleansing, a route with many stations close to the platform and the toilet may be guided. In addition, since the subject may feel more comfortable if a train with relatively many stops (such as a local train) is guided rather than a train with few stops, an appropriate travel route may be guided after confirming whether the subject wants to go to the toilet. When the departure alarm is notified, the subject starts traveling from his/her home to the medical facility. In addition, in the case where the application software of FIG. 2 is executed proactively on the user terminal 20, in this step, when the control unit 21 determines that it is time to depart, a departure alarm is displayed on the UI unit 24.

After the departure alarm is notified to the subject, the expected end time of the examination is obtained (S15). When the subject arrives at the medical facility, the endoscopic examination begins as soon as preparations are complete. The subject may arrive at the medical facility earlier than expected, and the examination may not start as scheduled because the examination of the subject in front of the subject takes time. In addition, when the subject is examined, if insertion is difficult, the examination may take longer than scheduled. In addition, if a polyp is found during the examination, the examination time may be extended due to polyp removal. Therefore, in this step, the time prediction unit 15 predicts the end time of the examination taking into account various changes in circumstances. The time prediction unit 15 predicts the time when the endoscopic examination of the subject will end in cooperation with the in-hospital systems 30 and 34.

In step S15, the time prediction unit 15 may infer the possibility of the presence of polyps based on the subject's profile, lifestyle, previous examination results, etc. Also, the time prediction unit 15 may infer the possibility of lesions other than polyps that require treatment such as resection or biopsy. That is, here, the possibility of treatments related to the risk of fluctuating the examination time during endoscopic examination is predicted, and the expected examination completion time is estimated based on the results.

2 is executed proactively on the user terminal 20, in this step S15, the control unit 21 inquires of and obtains the predicted test end time from the time prediction unit 15 in the service server 10. The control unit 21 may collect necessary information from the service server 10 and predict the test end time.

Next, the expected recovery completion time is acquired (S17). If a sedative is used in the examination, when the examination is completed, the subject rests in the recovery room until the sedative wears off. Information such as the congestion state of the recovery room can be acquired by the recovery room information acquisition unit 39. In this step, the control unit 11 queries the recovery room information acquisition unit 39 to acquire information such as the congestion state of the recovery room, and the time prediction unit 15 predicts the recovery completion time based on this information and the subject's physical condition, etc., and acquires this expected recovery completion time. Note that, when the application software in FIG. 2 is executed independently on the user terminal 20, in this step, the control unit 21 acquires the expected recovery completion time from the time prediction unit 15 in the service server 10. The control unit 21 may collect necessary information from the service server 10 and predict the recovery completion time.

Next, the estimated time of completion of the entire examination process is displayed (S19). Here, the control unit 11 causes the time prediction unit 15 to predict the time when the entire examination process will be completed based on the estimated times and the like obtained in steps S15 and S17, and obtains this estimated time. Since it takes time for waiting time, examination time, recovery time, explanation time of the examination results, time to receive medicine if any, and accounting time until the examination is completed, the time prediction unit 15 predicts the completion time of the entire examination process taking these times into consideration. The completion time of the entire examination process may be predicted taking into consideration the condition information of the subject of the endoscopic examination, the congestion state of the hospital where the endoscopic examination is performed, and the operating status of the staff. When the time of each process of the examination is predicted, it is transmitted to the user terminal 20 via the estimated time instruction transmission unit 16 and displayed on the UI unit 24. An example of the display of the estimated time of the entire process will be described later with reference to FIG. 9C. 2 is executed proactively on the user terminal 20, in this step the control unit 21 causes the time prediction unit 15 in the service server 10 to predict the completion time, obtains this predicted time, and displays it on the UI unit 24. The control unit 21 may also collect necessary information from the service server 10 to predict the completion time.

Once the estimated time of completion of the entire test process has been displayed, it is next determined whether the subject has left the hospital (S21). Here, the user terminal 20 uses a positioning system such as GPS to determine whether the subject has left the medical facility. If the result of this determination is that the subject is still in the medical facility, the process returns to step S15, the above-mentioned operation is performed, and the estimated time display of the entire test process is updated. In other words, since the end time of the test and the estimated recovery completion time at the medical facility change depending on the situation, in this step the completion time of the entire test process is predicted and displayed taking into account the change in situation. On the other hand, if the result of the determination in step S21 is that the subject has left the hospital, this main operation flow ends.

In addition, in step S21, in addition to leaving the hospital, it may be determined that the subject has gone to a parking lot, gotten in a car, started using public transportation, returned home, etc., as long as it is possible to determine the end of support by the peace of mind app. In addition, although FIG. 2 shows a return to step S15, if the test has already ended, it may be possible to return to step S17, and if recovery has already been completed, it may be possible to return to step S19.

In addition, when the application software in Figure 2 is executed independently by the service server 10, in step S21, the control unit 11 in the service server 10 requests the current location obtained by a positioning system such as GPS in the user terminal 20 to be transmitted to the service server 10.

In this way, in the flow of the main operation shown in FIG. 2, the expected start time of an examination such as an endoscopy is obtained, and based on this start time, the subject can be advised on the timing to start taking the cleaning agent (see S1, S3). In addition, when cleaning is completed, the expected start time of the examination according to the subsequent changes in the situation is obtained, and the departure time is calculated taking into account the time required to travel from the current location to the medical facility, and a departure alarm is issued when this departure time is reached (see S7 to S13). In addition, since it takes time such as waiting time, examination time, recovery time, explanation time of the examination results, and accounting time to complete the examination at the medical facility, the completion time of the entire examination process is predicted and displayed taking these times into consideration (see S15 to S19). Therefore, the subject can predict how long the examination will take and can undergo the examination with peace of mind.

In FIG. 2, the examination end time and recovery completion time are predicted based on information from the examination room information acquisition unit 37 and the recovery room information acquisition unit 39 (see S15, S17). However, it is not limited to this, and the start and end times of each procedure may be predicted and advice displayed using information on rooms (not limited to rooms, but may also be places (spaces)) within the medical facility, such as waiting rooms, pre-treatment rooms, rooms for notifying examination results (examination rooms, etc.), accounting rooms, etc., as well as the schedule of the endoscopist performing the examination, and other information.

Next, the operation of the cleanliness check in step S5 (see FIG. 2) will be described using the flowchart shown in FIG. 3A. As described above, in this embodiment, the subject takes the cleansing agent at home or elsewhere (excluding medical facilities) and intestinal cleansing is performed. In this cleanliness check flow, advice necessary for the subject to take the cleansing agent is provided, and further, based on the defecation image, it is determined whether or not sufficient cleansing has been performed.

When the cleanliness check flow shown in FIG. 3A starts, first it is determined whether cleansing is necessary and it is time to drink (S21). After the start drinking alarm is displayed in step S3 (FIG. 2), the subject must take the cleanser at a predetermined time interval. In this step, the control unit 11 determines whether the cleanser must be taken and whether a predetermined time has passed since the last time it was taken. To this end, the control unit 11 obtains information on the subject's cleanser intake status from the user terminal 20 and makes a determination. Note that when the application software in FIG. 2 is executed independently by the service server 10, the control unit 21 determines in step S21 whether the subject has taken the cleanser based on input information, etc., to the UI unit 24 of the subject.

Next, the ease of cleaning is inferred based on the subject's profile and lifestyle (S23). When the subject drinks a cleaning agent to cleanse the intestinal tract, the ease of cleaning varies from person to person, and it is advisable to display an appropriate guide when drinking the cleaning agent according to the ease of cleaning. Therefore, in this step, the ease of cleaning for the subject is inferred. The user's profile is recorded in the recording unit in the user terminal 20 or the test result recording unit 17 of the service server 10. In addition, since the user usually carries the user terminal 20, the user's behavior, for example, daily habits such as what time they get up, what time they have breakfast, lunch, and dinner, and what time they go to bed, can be known, and information such as walking, jogging, and exercising at the gym can also be obtained. Furthermore, if the contents of meals are photographed, it is also possible to know what they are eating. Furthermore, the test result recording unit 17 also records the results of various health checkups of the subject. Using this information, the ease of cleaning is inferred by inputting the above information using an inference engine provided in the service server 10.

When it is time to wash and drink as a result of the judgment in step S21, the amount of detergent is displayed (S25). Since the subject may not know how much detergent to drink, in this step, the control unit 11 transmits the amount of detergent stored in advance to the user terminal 20 through the instruction transmission unit 16 according to the detergent used by the subject for washing, and displays it on the UI unit 24. At this time, the amount of detergent to be drunk in a certain period of time may be adjusted according to the ease of washing. This display will be described later with reference to FIG. 9B. Note that, when the application software in FIG. 2 is executed independently by the service server 10, the control unit 21 displays the amount of detergent transmitted from the instruction transmission unit 16 on the UI unit 24. Alternatively, the control unit 21 may store in advance the amount of detergent to be drunk each time, and display the amount on the UI unit 24 based on the stored amount of detergent.

2 is executed independently by the service server 10, the control unit 11 may collect the above-mentioned information in steps S23 and S25 and infer the ease of cleaning based on this information. Also, the possibility of polyps (possibility of treatment related to the risk of fluctuating examination time) may be inferred. Also, if an inference engine is provided in the user terminal 20, this inference engine may be used in step S23. In this embodiment, the inference engine is used to infer the ease of cleaning (which may be the presence or absence of polyps), but the ease of cleaning (which may be the presence or absence of polyps) may also be predicted (determined) logically.

Next, it is determined whether or not the subject has difficulty in drinking the cleaning agent (S27). As mentioned above, the ease of drinking the cleaning agent varies from person to person, and some subjects complain that the cleaning agent is difficult to drink. If the subject finds it difficult to drink the cleaning agent, the subject can express his/her intention by clicking a give-up icon on the screen of the UI unit 24 of the user terminal 20. In this step, the control unit 11 confirms the subject's intention expressed on the user terminal 20. Note that, when the application software of FIG. 2 is executed independently on the service server 10, the control unit 11 may confirm the subject's intention expressed on the UI unit 24. In addition to the give-up icon, the user terminal 20 may be provided with a give-up button or the like so that the subject can operate the give-up button or the like.

If the result of the judgment in step S27 is that it is difficult to drink, the hospital checks the image via the server and inputs whether or not it is necessary to drink any more cleansing agent (S29). To perform an endoscopic examination, it is necessary to make sure that there is no stool remaining in the intestines, and this can be seen by looking at the image of the subject's defecation. Therefore, in this step, the subject's defecation is photographed by the imaging unit 23 of the user terminal 20, and this image is sent to the in-hospital system 30 or 34 via the service server 10 (see screen D4 in Figure 9B). The doctor in charge, etc., looks at the transmitted image and judges whether or not it has been sufficiently cleansed, and judges whether or not it is necessary to drink any more cleansing agent. At this time, it is possible to have a dialogue by voice or the like so that the subject and the hospital can communicate with each other.

Next, based on the image judgment in step S29, it is judged whether the hospital judged that the endoscopy was sufficiently cleaned (S31). In this step, the judgment is made based on the judgment of the doctor in charge of the endoscopy. The result of this judgment is transmitted to the control unit 21, and if it is judged that the endoscopy was sufficiently cleaned, the process proceeds to step S47, where the completion of cleaning is displayed. On the other hand, if it is judged that the endoscopy was not sufficiently cleaned, the hospital's judgment result is displayed (S33). Here, the control unit 11 transmits the judgment result from the in-hospital system 30 or 34 to the user terminal 20, and displays the hospital's judgment result, that is, the need to drink a cleaning agent, on the UI unit 24 of the user terminal 20. In addition, if the hospital judges that an abnormal situation exists, it may display a message to visit the hospital immediately. In addition, when the application software in FIG. 2 is executed independently on the user terminal 20, the control unit 11 obtains the judgment result of the doctor in charge of the hospital through the service server 10, and displays the judgment result on the UI unit 24.

If it is not difficult to drink in step S27, or if the hospital's judgment result is displayed in step S33, the process waits until the cleansing agent is completely drunk (S35). Here, the process waits until the subject drinks the cleansing agent amount displayed in step S23. When the subject drinks the cleansing agent, this can be input to the user terminal 20. During the waiting period until the cleansing agent is completely drunk, the subject may defecate. In this case, although not shown in FIG. 3A, the process proceeds to step S39 and the process from S39 onwards is carried out. When the cleansing agent is completely drunk, the process returns to step S21 and a determination is made as to whether it is time to drink the next cleansing agent. When it is time to drink the cleansing agent, the process from step S23 onwards is carried out.

If the result of the determination in step S21 is that cleansing agent is necessary but it is not time to drink it, then it is determined whether or not a bowel movement has occurred (S37). Drinking the cleansing agent may cause the subject to have a bowel movement. If a bowel movement has occurred, this is input to the UI unit 24 of the user terminal 20 (see screen D4 in Figure 9B). If a bowel movement has not occurred, the system remains on standby and returns to step S21. Here, the control unit 11 makes the determination based on information from the user terminal 20.

On the other hand, if defecation has occurred, an image is captured (S39). In this case, the control unit 11 instructs the subject to capture an image of the defecation using the imaging unit 23 of the user terminal 20. At this time, the control unit 21 displays advice on the UI unit 24 for the subject to capture an image (see screen D4 in Figure 9B). Note that when the application software in Figure 2 is executed independently on the user terminal 20, the control unit 11 instructs the subject to capture an image of the defecation.

The image capturing in step S39 is not limited to capturing an image, and if a sensor such as a camera is connected to the toilet bowl, the image and information related to defecation may be obtained from the sensor. Also, various information other than optical physical quantities, such as the liquid properties of the water in the flush toilet, electrical resistance, and ultrasonic reflectance, may be obtained in step S39, and the degree of cleansing of the intestinal tract may be determined using changes over time.

Once an image has been captured, it is transmitted (S41). Here, in order to determine the level of cleanliness, the control unit 11 instructs the user terminal 20 to transmit the captured image to the service server 10. If the application software in FIG. 2 is executed independently on the user terminal 20, the control unit 11 transmits the image once a defecation image has been captured. If the user terminal is capable of performing a cleanliness check, the image may be determined within the user terminal 20 without being transmitted to the service server 10.

Next, the cleansing level is obtained (S43). In the service server 10 which has obtained the image of the defecation, the cleansing level determination unit 14 determines the cleansing level of the subject's intestinal tract. As the cleansing of the intestinal tract progresses, the defecation image becomes clearer, and the cleansing level can be obtained by analyzing the image. How the cleansing level is determined will be described later with reference to Figures 4 to 8. Note that when the application software in Figure 2 is executed independently on the user terminal 20, the control unit 21 obtains the cleansing level determination result from the cleansing level determination unit 14.

Once the cleaning level has been obtained, it is then determined whether or not sufficient cleaning has been achieved (S45). Here, the control unit 11 obtains the latest cleaning level determination result from the cleanliness determination unit 14 and makes a determination based on this. Note that when the application software in FIG. 2 is executed independently on the user terminal 20, the control unit 21 may make a determination based on the latest cleaning level determination result from the cleanliness determination unit 14, or may obtain the determination level in the cleanliness determination unit 14.

If the result of the judgment in step S45 is that the cleaning level is not sufficient, the expected time to finish cleaning and the remaining amount to drink (number of times) are displayed (S49). Here, the time prediction unit 15 predicts the amount (number of times) of cleaning agent required to reach the level at which cleaning is completed and the end time. Alternatively, the time prediction unit 15 queries the schedule management unit 13 or the cleanliness prediction unit 14 in the service server 10 to obtain the prediction result. The UI unit 24 is configured to display the amount of cleaning agent that the subject drinks each time (see, for example, screens D2a to D2c in FIG. 9B), and considering the measurement result of the cleanliness of the subject, it is possible to estimate the timing at which the cleaning level of the subject reaches the testable level Lev (see, for example, FIG. 4). If the end timing can be predicted, the amount (number of times) of cleaning agent to be drunk by that time can be predicted. This prediction of the end timing may be a classical logical method (see, for example, FIG. 4, FIG. 7, etc.), or may be a method of inference using artificial intelligence AI (see, for example, FIG. 6, FIG. 8, etc.).

When the predicted results of the amount (number of times) of detergent and the end time are acquired, the instruction transmission unit 16 transmits the predicted results to the user terminal 20 and causes the UI unit 24 to display them. When this display is performed, a message of encouragement to the subject, such as "Good luck," may also be displayed. When the predicted results of the amount (number of times) of detergent and the end time are displayed on the UI unit 24, the process returns to step S21.

In addition, in step S49, when the application software of Figure 2 is executed proactively on the user terminal 20, the control unit 21 collects the necessary information, predicts the amount (number of times) of cleaning agent required to reach the cleaning completion level and the end time, and displays this prediction result on the UI unit 24.

On the other hand, if the result of the judgment in step S45 is that sufficient cleaning has been performed, or if the hospital judges in step S31 that sufficient cleaning has been performed, a cleaning completion display is performed (S47). Here, the instruction transmission unit 16 causes the UI unit 24 of the user terminal 20 to display that cleaning has been completed (see screen D6 in FIG. 9B). Since cleaning has been completed, the subject can move to the hospital and undergo an endoscopic examination there (see S11 and S13 in FIG. 2). When the cleaning completion display is performed, the cleanliness check flow ends and the original flow is returned to.

Next, a modified example of the cleanliness check will be described using the flowchart shown in FIG. 3B. In the flowchart shown in FIG. 3A, if the result of the judgment in step S21 is that it is time to drink the cleanser, a series of steps from step S23 onwards are executed, and if it is not time to drink, it is judged whether or not there is a defecation. In contrast, in the flow shown in FIG. 3B, if the result of the judgment in step S21 is that it is time to drink the cleanser, the amount of cleanser is displayed and then the flow returns to step S21, while if it is not time to drink the cleanser, a series of steps from step S27 onwards are executed. That is, the flow is the same as that of FIG. 3A, except that the destination when the result of the judgment in step S21 is No, the destination after executing step S23, and the destination after executing step S33 are different. The operation in each step is the same as that of FIG. 3A, so a detailed explanation is omitted.

3A and 3B, when the subject drinks the cleansing agent and undergoes intestinal cleansing, a defecation image is acquired (see S39, S41), the cleansing level is determined based on this defecation image (see S43), and advice is given to the subject based on the change in the cleansing level over time (see S47, S49). This makes it possible to give advice tailored to the condition of each individual subject.

In addition, in the flow of the cleansing check shown in FIG. 3A and FIG. 3B, when it is time for the subject to take the cleansing agent, the subject is advised to that effect (see S21, S23). The cleansing agent must be taken at a specified time interval, and some subjects find time management troublesome, but even such subjects can take the cleansing agent without any problems in this flow. In addition, if the subject feels that it is difficult to take any more cleansing agent and operates the give up icon (S27 Yes), an image of the defecation is sent to the hospital, and the degree of cleansing is judged at the hospital (see S29, S31). Although the degree of cleansing of the image may be judged using AI, in this embodiment, the person in charge at the medical institution judges it, so that the subject can be reassured. In this case, if the result of the intestinal cleansing indicates that the intestines are clean, "cleaning completed" is displayed (S47), and on the other hand, if the intestines are not clean, an encouraging message such as "keep up the good work" may be displayed.

Next, the acquisition of the cleaning level in step S49 and the prediction of the cleaning degree in step S49 will be explained using Figures 4 to 8.

First, the prediction of the cleanliness level will be explained using FIG. 4. FIG. 4 is a graph showing the change in cleanliness level, with the horizontal axis showing the time since the start of taking the cleansing agent, and the vertical axis showing the cleanliness level. The cleanliness level on the vertical axis is low on the upper side and high on the lower side, and if the cleanliness level is below the level Lev, the test is possible. The solid line is the standard cleanliness level curve Lst, which shows the standard change in cleanliness level since the start of taking the cleansing agent. It is considered that the change in cleanliness level has an approximate tendency depending on the movement of the intestines. Therefore, the meal contents and time, the amount and time of stool are collected, the cleanliness level is predicted, and the standard cleanliness level curve Lst is created. This standard cleanliness level curve Lst may be created using AI, or may be created using a classical method (logical method, etc.). It may also be changed based on the previous performance, gender, age, race, region, etc. It may also be changed according to the cleansing agent used. The creation of this standard cleanliness level change curve will be described later using FIG. 5 to FIG. 8.

In the graph of Figure 4, Ra1 to Ra5 are the measured cleanliness levels of subject a at each time, and the dashed dotted line is a predicted change curve Lesa of subject a's cleanliness level, which was created by taking into account subject a's measured values Ra1 to Ra5 with respect to the standard cleanliness level curve Lst. Using this predicted change curve Lesa, subject a's cleanliness level at each time can be predicted, with Ra6 and Ra7 being the predicted values. In this example, the amount of detergent cannot be reduced, but a sense of security can be provided by displaying how much more detergent is left to drink before the prescribed amount is consumed.

The dashed line is an alert line La for determining whether or not the degree of cleansing is insufficient when the subject drinks the cleansing agent to cleanse the intestinal tract. If the subject's measurement value is to the right of the alert line La, i.e., if the degree of cleansing is insufficient, a warning is issued to the subject. In the example shown in Figure 4, the measurement value Ra5 is to the right of the alert line La, exceeding the alert line La and indicating an insufficient degree of cleansing, so the service server 10 issues an alert to the hospital.

In the graph of FIG. 4, Rb1 and Rb2 are the measured values of the cleanliness of subject b at each time. As in the case of subject a, a predicted change curve Lesb of the cleanliness can be created by considering the measured values Rb1 and Rb2 of subject b with respect to the standard cleanliness curve Lst. Using this predicted change curve, the cleanliness of subject b at each time can be predicted, and Rb3, Rb4, and Rb5 are the predicted values. In the case of subject b, the cleanliness is more advanced than the standard cleanliness curve Lst, so it is predicted that the cleanliness will be completed even if the amount is less than the specified amount. In other words, it is possible to roughly estimate from the actual cleanliness curve and display how much should be drunk.

Thus, the time it takes to reach the testable level Lev varies from subject to subject, and in the example shown in FIG. 4, there is a difference in the time difference Tdif. In general, a prescribed amount of intestinal cleansing agent is taken at regular intervals, so the amount of cleansing agent taken can be reduced roughly in proportion to the time difference Tdif. In this embodiment, the cleansing level is measured, and the time it will take to reach the testable level or below is predicted taking into account individual differences, and presented to the subject. That is, it is possible to predict how much time will be required and how many times the cleansing agent will be taken from the standard cleansing level curve Lst and the actual measured value. In addition, based on this prediction, the UI unit 24 of the user terminal 20 may display a message with a sense of monitoring, such as "You're doing well" or "It's a little late, but do you want to talk to me?"

Next, the cleanliness prediction algorithm will be described. First, the creation of the standard cleanliness curve Lst will be described. To create the standard cleanliness curve, the following data is obtained from N subjects.
- Basic information of the subject (age, gender, weight, height, presence of constipation, etc.)
- Images and time of defecation after drinking detergent

Once the above data has been obtained from the N subjects, the cleanliness is then quantified. To quantify the cleanliness, an image feature F is extracted from the stool image. The image feature F may be a vector value in the RGB color space or an average value of RGB. Features may also be calculated using other color spaces. Furthermore, solid stool may be extracted and the occupancy rate of the solid matter in the image may be used as the feature.

The sequence of stool images up to the point where general inspection is possible will be called the reference image sequence. Figure 5 shows an example of a reference image sequence. In this example, image P1 of the subject's initial stool shows a solid state, subsequent image P2 shows a muddy state, and subsequent image P3 shows a state with residue. Image P4 in an inspectable state is a transparent liquid. In the reference image sequence, the image feature amount for image P4 in an inspectable state is F_ok. The feature amount F_j of each stool image and its distance in the feature amount space from F_ok are denoted as D_j, and this is defined as the degree of cleanliness. In other words, the smaller the value of D, the closer the state is to being inspectable.

After creating a reference image sequence and calculating the features of each image, a standard cleanliness curve is then created. First, image data for N subjects is plotted in the cleanliness-time space. Patterns with clearly different decreasing trends are recorded as different standard patterns (see, for example, the standard cleanliness curve Lst in Figure 4). At that time, basic subject information that defines the group is identified using the group of patterns of the standard cleanliness curve. By carrying out these processes, a standard cleanliness curve Lst is created. It is also possible to create different standard cleanliness curves Lst based on the age, gender, etc. of the population.

The above-mentioned cleaning curve initially varies greatly depending on the mass being excreted, and then changes to a mode in which the matter adhering to the surface of the intestine is washed away by the cleaning agent, so that it may be determined by the attenuation of the first mode determined by the cross-sectional area of the intestine in the luminal direction, and the attenuation of the second mode determined by the surface area of the inner surface of the intestine. The shape and size of the intestine may be classified and determined based on the subject's profile.

It is also known that whether or not the examinee has paid attention to their diet up until the day of the examination affects whether or not residues are likely to remain. For example, in terms of noodles, udon, somen, and hiyamugi are easy to digest and do not leave residues, while soba, ramen, and pasta are known to be more likely to be affected. It is known that people who have consumed these recommended foods can take the examination with less intestinal cleansing agent than people who have eaten other foods. In other words, information regarding the completion timing of the process related to the preparation for the endoscopic examination may be generated according to the dietary history information of the examinee or subject. In addition, the threshold level for the examination being OK may be corrected according to the dietary history information, and a guide for the completion of the cleansing may be presented by correcting the expected time and the amount of intestinal cleansing agent. The above-mentioned history information may be self-reported by inputting it into a smartphone or terminal, or may be input into the system by oral report by a medical professional, or may be determined from a menu image taken with a smartphone.

The above-mentioned cleansing curve depends on the clumps and liquid color in the bowel movement, and after solid excrement is released, only liquid remains, and eventually only residue is visible. Although it is called liquid, it is not a solution, so it eventually turns muddy after visible solids are released. In this way, the cleansing curve can be classified into classes where there is solid (Class 1), muddy (Class 2), and liquid + residue (Class 3). A curve based on the correlation between the duration of Class 1 (or Class 2 as well) and the time change of Class 3 can be used as a substitute for the cleansing curve, or to predict the end of cleansing.

In addition to the method of defining the cleanliness from the image feature value F and creating the standard cleanliness curve Lst as described above, the cleanliness may be defined using deep learning. This method is explained using Figure 6.

Figure 6(a) shows a method for collecting defecation image data and generating an inference model. First, a large number of defecation image data are collected, corresponding to the subject's basic information and the time since they started taking the cleanser. Next, an annotator (a person who adds annotations) refers to the reference image sequence (see Figures 5 and 6(b)) for the collected image data, determines which class it falls into, adds the determined class as an annotation, and creates training data. Using the example of Figure 6, the annotator looks at defecation image P11, refers to the reference image sequence (images P1 to P4) shown in Figure 6(b), and determines which image (class) defecation image P11 is closest to.

In the example shown in Figure 6 (b), the cleanliness levels of images P1 to P4 are 100, 66, 33, and 0, respectively. In Figure 6 (a), the defecation image P11 is annotated A11 with "No. 2" from when it is close to "Muddy state 2" in image P2, and training data T11 is created. Training data is created by annotating all of the collected image data.

Once training data is created from N subjects, an inference model is created that predicts the stool image and class using this training data. This may be replaced with the distance D in the feature space of the classical method. Once this inference model is created, a standard cleanliness curve Lst can be obtained from the stool images of many subjects. In other words, a standard cleanliness curve Lst (see Figure 4) can be obtained that shows how the cleanliness changes over time when a standard subject drinks a cleanser. A standard cleanliness curve Lst may be created for each subject profile, such as the gender and age of the subjects who are the parent set of training data.

When the standard cleanliness curve Lst is created by quantifying the cleanliness from the image feature F as described above, or when the standard cleanliness curve Lst is created using deep learning as described with reference to FIG. 6, the system then selects a standard cleanliness curve Lst suitable for the user based on the subject's (user's) basic information (e.g., gender, age group, etc.). FIG. 7(a) shows an example of a standard cleanliness curve Lstc suitable for user c. In FIG. 7(a), as in the graph of FIG. 4, the vertical axis indicates the cleanliness and the horizontal axis indicates the elapsed time since the subject started taking the detergent. In addition, from the standard cleanliness curve Lst of M patterns created based on basic information such as representative age and gender, the standard cleanliness curve Lst of N patterns (N≦M) close to the subject's basic information may be selected, and the standard cleanliness curve Lstc of the subject may be created by performing interpolation or the like.

Once the standard cleanliness curve Lstc suitable for the subject has been selected, the subject then drinks the cleansing agent and sends a defecation image to the service server 10 each time he or she defecates (see S37 to S41 in Figures 3A and 3B). When the service server 10 receives the defecation image, the cleanliness assessment unit 14 calculates the cleanliness from the defecation image. This calculation may be performed by the cleanliness assessment unit 14 comparing the defecation image with the reference image sequence shown in Figure 5, or may be performed using an inference model for inference. The cleanliness D may also be calculated using image features.

Once the cleanliness assessment unit 14 has calculated the cleanliness, it plots the cleanliness on a graph. Once multiple cleanliness levels have been plotted, a predicted curve is calculated and displayed as time-series data. In the example shown in FIG. 7(b), the cleanliness levels Rc1, Rc2, and Rc3 are calculated for each defecation image of subject c. Next, a predicted curve Lc of the cleanliness level of subject c is obtained based on the cleanliness levels Rc1, Rc2, and Rc3.

An AR model or a Kalman filter may be used as an algorithm for calculating the predicted curve of the subject's cleanliness. A predicted curve may also be calculated by fitting a standard cleanliness curve to the subject's plotted data. Once the predicted curve is calculated, it is possible to calculate how many more times the subject needs to drink the cleanser before the test can be completed, and the number of times to drink can be notified to the subject (see S49 in Figures 3A and 3B).

Next, with reference to FIG. 8, an example of predicting the cleanliness level using deep learning instead of classical methods will be described. To infer the cleanliness level using deep learning, first, an inference model is created using deep learning. FIG. 8(a) shows the deep learning algorithm. First, images of N pieces of stool from people who have drunk a cleaning agent are acquired (#1). For example, the service server 10 acquires images of the stool of subjects through multiple user terminals. With the consent of the subjects, the images acquired in S41 of FIG. 3A and FIG. 3B may be used.

Once the defecation images have been collected, information is then added to each image up to the point where it is ready for testing, and learning is performed (#3). Information up to the point where it is ready for testing includes the amount of cleanser taken, the number of times, the time, etc., before it is ready for testing. Figure 8 (c) shows an example of a defecation image of subject A. Images PA1, PA2, ... are defecation images of subject A, with image PA1 on the left being the first image, and changing to images PA2, PA3, ... as time passes, with the final image PAN indicating that the defecation has been sufficiently cleaned for endoscopic testing. Similarly, images PB1, PB2, PB3, PBN are defecation images of subject B, and the final image PBN indicates that the defecation has been sufficiently cleaned.

In step #3, subject A's test is OK at the image PAN stage, and subject B's test is OK at the image PBN stage, so the information that is obtained until the test is possible is the amount of cleanser that subjects A and B have drunk, the number of times, the time, etc., up to this stage. For example, at the defecation image PA1 stage, subject A's information obtained until the test is possible is that he or she needs to drink another 1500 ml, drink another 10 times, or take another 2 hours, etc.

Once information such as the amount of detergent required (cleaning time) until the image is ready for testing is obtained, the information required until the image is ready for testing is annotated onto each stool image to create training data. Since images of N people's stool are collected, N pieces of training data can be created. Once the training data is ready, the stool image is input into the inference engine, and an inference model is generated by changing the weighting of the connections for each input of the inference engine's neural network so that the information required until the image is ready for testing can be output. When the inference model is generated, the learning in Figure 8(a) ends.

When the inference model is generated, the predicted time of the end of the cleaning in step S49 of FIG. 3A and FIG. 3B and the amount (number of times) of the cleaning agent to be drunk until then can be inferred. The algorithm of this inference is explained with reference to FIG. 8(b). First, the subject (user) undergoing an endoscopic examination sends a defecation image to the system (#11). As explained in the flow of FIG. 3A and FIG. 3B, when the subject drinks the cleaning agent and defecates, the subject is advised to take an image of the defecation and send the image (see S37, S39, S41 of FIG. 3A and FIG. 3B, and screen D4 of FIG. 9B). This defecation image is sent to the service server 10.

Next, based on the received image, the amount of detergent, the number of times, and/or the time until the test can be performed is estimated (#13). When the service server 10 receives the defecation image data in step S41 of Figures 3A and 3B, the cleanliness assessment unit 14 inputs the received defecation image data to the inference engine. For this purpose, the inference model described with reference to Figure 8 (a) is set in the inference engine. When the defecation image data is input to the inference engine, it is possible to estimate the amount of detergent, the number of times, and/or the time until the test can be performed. When the inference result is output, the expected time when the cleaning will end and the amount (number of times) of detergent to be drunk until then can be displayed on the display unit of the UI unit 24, etc. (see, for example, screen D5 of Figure 9B).

Here, we will explain the deep learning performed in FIG. 8(a) and other figures. "Deep learning" is a multi-layered structure of the "machine learning" process using neural networks. A typical example is a "forward propagation neural network" that sends information from front to back and makes a judgment. The simplest forward propagation neural network has three layers: an input layer consisting of N1 neurons, a middle layer consisting of N2 neurons given by parameters, and an output layer consisting of N3 neurons corresponding to the number of classes to be discriminated. Each neuron in the input layer and middle layer, and the middle layer and output layer are connected by a connection weight, and a bias value is added between the middle layer and output layer, making it easy to form a logic gate.

A neural network can have three layers if it is to perform simple discrimination, but by adding multiple intermediate layers, it is possible to learn how to combine multiple features during the machine learning process. In recent years, neural networks with 9 to 152 layers have become practical in terms of the time it takes to learn, the accuracy of discrimination, and energy consumption. In addition, a "convolutional neural network" that compresses image features, operates with minimal processing, and is strong in pattern recognition, may be used. In addition, a "recurrent neural network" (fully connected recurrent neural network) that can handle more complex information and allows information to flow in both directions may be used to handle information analysis in which the meaning changes depending on the order or sequence.

To realize these technologies, conventional general-purpose arithmetic processing circuits such as CPUs and FPGAs (Field Programmable Gate Arrays) may be used. However, this is not limited to this, and since most neural network processing is matrix multiplication, processors specialized for matrix calculations such as GPUs (Graphic Processing Units) and Tensor Processing Units (TPUs) may also be used. In recent years, such artificial intelligence (AI) dedicated hardware "neural network processing units (NPUs)" have been designed to be integrated and embedded with CPUs and other circuits, and may become part of the processing circuit.

Other machine learning methods include, for example, support vector machines and support vector regression. The learning here involves calculating the weights, filter coefficients, and offsets of the classifier, and there are also methods that use logistic regression processing. When making a machine make a judgment, a human needs to teach the machine how to make the judgment. In this embodiment, a method is used in which an image judgment is derived by machine learning, but a rule-based method that applies rules acquired by humans through experience and heuristics may also be used.

Next, the advice provided to the subject in the UI unit 24 of the user terminal 20 will be described with reference to Figs. 9A to 9C. Fig. 9A is an example of an alarm to start drinking, which is performed in step S3 in Fig. 2. As described above, the flow shown in Fig. 2 shows the operation when the subject drinks a cleaning agent at home or the like to undergo an endoscopic examination, cleans the intestines, and then goes to the hospital to undergo the examination. In this flow, the advice to start drinking is presented to the subject when it is time to start drinking, calculated backwards from the start time of the examination (see S3 in Fig. 2). Screen D1 in Fig. 9A shows an advice image at this time, and in this example, "Please start drinking. It will take more than two hours to finish drinking. Please start drinking soon." is displayed on the UI unit 24. At this time, the required time may be changed and displayed using past cleaning time results, etc. For example, if the degree of cleaning was sufficient for the examination in one and a half hours in the past, it may be displayed that it takes about one hour and 45 minutes.

Figure 9B shows advice displayed on the UI unit 24 of the user terminal 20 from the start of taking the cleanser until cleaning is completed. Screens D2a, D2b, and D2c are screens that display the amount of cleanser. Since there are several types of cleanser, the display is based on the cleanser given to the subject. Screen D2a displays how to take the cleanser when the cleanser is MoviPrep (registered trademark), screen D2b displays how to take the cleanser when the cleanser is Moven (registered trademark), and screen D2c displays how to take the cleanser when the cleanser is VisiClear (registered trademark).

In addition, an icon Iph is displayed at the bottom left of screens D2a, D2b, and D2c for making voice calls to the hospital where the subject will be examined. If the subject feels that the cleansing agent is difficult to swallow, he or she can touch this icon Iph to call the hospital and speak to the person in charge. Screen D3 shown in Figure 9B is the screen displayed when the hospital is called. When this screen D3 is displayed, the subject can talk to the person in charge and have them show an image of the defecation, allowing the person to decide whether to continue taking the cleansing agent, stop, or interrupt and go to the hospital (see S29 in Figures 3A and 3B).

In addition, a toilet icon Ito is displayed at the bottom right of screens D2a, D2b, and D2c, allowing the subject to receive advice when he or she goes to the toilet to defecate. When the subject touches the toilet icon Ito, screen D4 is displayed, prompting the subject to take a picture of the defecation (see S39 in Figs. 3A and 3B).

When the subject sends a defecation image to the service server 10, the cleanliness assessment unit 14 assesses the cleanliness level based on the received image. The cleanliness assessment unit 14 also predicts the amount (number of times) of cleansing agent to be consumed until the intestines are sufficiently cleansed for an endoscopic examination, and the end time. The instruction transmission unit 16 in the service server 10 sends the prediction result to the user terminal 20, and causes the UI unit 24 to display it as shown on screen D5. In this example, it is predicted that 33% of the required amount will be consumed, 8 cups of cleansing agent will be consumed, and the cleansing will end at around 10:55. At this time, the service server 10 may also notify the hospital's in-house system 31 or 34 of the subject's intestinal cleansing level.

Furthermore, when the subject sends an image of defecation to the service server 10 and the cleanliness assessment unit 14 determines that sufficient cleansing has been achieved (see S47 Yes in Figures 3A and 3B), the instruction transmission unit 16 causes the user terminal 20 to display that cleansing has been completed. In the example shown in Figure 9B, a completion display is displayed on the UI unit 24, as shown on screen D6. At this time, the service server 10 may also notify the hospital's in-house system 31 or 34 that the subject's intestinal cleansing has been completed.

Figure 9C shows advice displayed on the UI unit 24 of the user terminal 20 when the subject leaves home or the like to travel to the hospital after intestinal cleansing with a cleaning agent is completed. When intestinal cleansing with a cleaning agent is completed, a display such as that shown in screen D6 of Figure 9B is displayed. Thereafter, when it is time to depart taking into account the travel time to the hospital and the start time of the examination, etc., a departure alarm is displayed on the user terminal 20 (see S11 and S13 in Figure 2). Screen D7 of Figure 9C shows an example of the departure alarm display. The departure alarm not only notifies the subject that it is time to depart, but may also acquire and display reference information for travel, such as traffic conditions from home or the like to the hospital.

When the subject arrives at the hospital, the UI unit 24 displays the examination schedule as shown in screen D8 (see S19 in FIG. 2). The schedule management unit 13 and the time prediction unit 15 predict all steps of the subject's examination, and the instruction transmission unit 16 causes the UI unit 24 to display the examination schedule. In addition, for the steps that have already been completed, the screen D8 may display the time based on the actual results by changing the character size, color, or font (display that allows the predicted time and the actual time to be distinguished). For example, when the subject arrives at the hospital, if the scheduled start time of the examination is 13:00 and the scheduled end time of the examination is 13:30, it is displayed as shown in screen D8. After that, if the time prediction unit 15 predicts the scheduled start time of the examination to be 13:05 based on the congestion status of the examination room, the predicted time is displayed. Also, if the examination actually starts at 13:04 and ends at 13:20, the screen D8 displays those times. Also, when the examination is completed 10 minutes earlier than the initial examination completion time, the time prediction unit 15 predicts the time forward taking this time into consideration, and the time predicted forward by the time prediction unit 15 is displayed.

As described above, in the first embodiment of the present invention, the subject takes a cleaning agent at home or the like other than a medical facility, cleans the intestines, and then moves to a medical facility to undergo an endoscopic examination. When undergoing this examination, status information such as where the subject is (S5 in FIG. 2) and what the cleaning status is (see S7 in FIG. 2, S43, S45 in FIG. 3A and FIG. 3B, etc.) is obtained, and the progress of the examination is estimated based on this status information (see, for example, S9, S15, S17, S19 in FIG. 2, S49 in FIG. 3A and FIG. 3B, etc.), and advice is provided based on this progress (see S13 in FIG. 2, S49 in FIG. 3A and FIG. 3B, etc.). This allows the subject to receive the advice necessary to undergo the endoscopic examination appropriately, and the subject can undergo the examination with peace of mind.

Next, a second embodiment of the present invention will be described with reference to Figures 10 to 12. In the first embodiment, the subject drinks the cleaning agent not at a hospital but at home or the like, and after intestinal cleansing is completed, moves to the hospital and undergoes an endoscopic examination. In contrast, in the second embodiment, the subject moves to the hospital, drinks the cleaning agent there, and after intestinal cleansing is completed, an endoscopic examination is performed. Therefore, the first and second embodiments differ in where the subject undergoes intestinal cleansing.

Figure 10 is a block diagram showing the overall configuration of an endoscopic examination support system according to the second embodiment. As in the first embodiment, this endoscopic examination support system is composed of a service server 10, a user terminal 20, and in-hospital systems 30 and 34. The internal configurations of the service server 10, the user terminal 20, and the in-hospital system 30 are similar to those of the first embodiment shown in Figure 1, and therefore will not be described.

The in-hospital system 34 has a control unit 35, a communication unit 36, a waiting room information acquisition unit 37, an examination room information acquisition unit 38, and a recovery room information acquisition unit 39, and each of these units has the same configuration as in the first embodiment shown in Figure 1, so detailed explanations will be omitted. In addition to the above-mentioned units, the in-hospital system 34 has a pre-treatment room information acquisition unit 40. In the second embodiment, the subject is transported to the hospital and undergoes pre-treatment such as drinking a cleaning agent.

The pretreatment room is a room where the subject takes a cleaning agent and other pretreatments before an endoscopic examination. The pretreatment room information acquisition unit 40 acquires information about the pretreatment performed by the subject. The pretreatment room information acquisition unit 40 has an imaging unit such as a camera and an audio collection unit such as a microphone arranged in the pretreatment room, and acquires various information about the pretreatment performed by the subject in the pretreatment room. Information in the pretreatment room is acquired based on the information collected by the imaging unit and audio collection unit. In addition, if the pretreatment information acquisition unit 40 can directly communicate with the user terminal 20, it can detect that the subject with the user terminal 20 is in the pretreatment room. Furthermore, it is possible to acquire various information such as what kind of examination the subject has come to receive, what kind of pretreatment is scheduled to be performed, and the subject's profile. In addition, a nurse or the like may directly input information as text or images from an in-hospital terminal. Instead of the user terminal 20, the hospital may lend a dedicated information terminal to the subject to collect information about the subject.

Next, the main operation of the application software for assisting the subject (user) in the endoscopic examination in cooperation with the user terminal 20 and the service server 10 in the second embodiment will be described using the flowchart shown in FIG. 11. As in the first embodiment, the main operation of this application is realized by the control unit 11 in the service server 10 cooperating with the control unit 21 in the user terminal 20 to control each unit in the service server 10, and further cooperating with the in-hospital systems 20 and 35. The flow shown in FIG. 11 is described on the premise that when a safety application for examination assistance is installed in the user terminal 20, the service server 10 takes the lead in providing advice to the user terminal 20 for the subject. In this case, the service server 10 functions as an examination guide device. However, it is of course possible for the user terminal 20 to take the lead in providing advice to the subject with the support of the service server 10. In this case, the user terminal 20 functions as an examination guide device.

As mentioned above, the second embodiment is significantly different in that the subject drinks the cleaning agent at the hospital. Therefore, compared with the main operation flow of FIG. 2 according to the first embodiment, steps S1 to S5 and S9 that the subject performed at home have been omitted, and step S10 has been added instead. In addition, steps S14a to S14c have been added as processing after the subject arrives at the hospital (note that S14c in FIG. 11 is the same as S5 in FIG. 2), steps S15 and S17 have been omitted, and step S23 has been added. Therefore, the flowchart of FIG. 11 will be explained, focusing on the differences from the first embodiment.

When the main operation shown in FIG. 11 starts, first the current location is acquired (S7). As described above, in this embodiment, it is assumed that the subject travels to a medical facility such as a hospital, where he or she takes a cleansing agent and undergoes intestinal cleansing. In this step, in order to calculate the time required to travel to the medical facility, the current location of the subject is first acquired. For this reason, the control unit 11 instructs the service server 10 to transmit information regarding the subject's current location acquired by a position detection unit such as GPS in the user terminal 20, and acquires the subject's current location. Note that when the application software in FIG. 11 is executed independently by the user terminal 20, in this step the control unit 21 acquires current location information by a position detection unit in the user terminal 20.

After acquiring the current location information, the estimated pretreatment start time is acquired (S10). Here, in order to calculate the time when the subject leaves his/her home, etc., the time when the pretreatment, such as taking a cleaning agent at a hospital, is to start is acquired. This pretreatment start time can be acquired by the control unit 11 inquiring of the schedule management unit 33 of the hospital or the like where the endoscopic examination is performed. The control unit 11 may inquire of the schedule management unit 33 about the scheduled examination start time, and the time prediction unit 15 may predict the estimated pretreatment start time by calculating backwards from the scheduled examination start time, and the estimated pretreatment start time may be acquired based on the reference time managed by the schedule management unit 33 and the time predicted by the time prediction unit 15 based on the time required for the process to be performed in the hospital. Note that, when the application software in FIG. 11 is executed independently on the user terminal 20, in this step, the control unit 21 inquires of the schedule management unit 33 through the service server 10 to acquire the estimated pretreatment start time.

Next, it is determined whether the departure time has arrived (S11). Here, the control unit 21 calculates the start time of movement to the medical facility based on the current location information acquired in step S7 and the information on the estimated pre-treatment start time acquired in step S10. In order to calculate this departure time, the subject's preference for transportation from home to the hospital may be set and the departure time may be calculated. In addition, the calculation may take into account the traffic conditions in front of the medical facility. After calculating the departure time, it is determined whether the current time is the calculated start time of movement (departure time). If the result of this determination is that it is not the departure time, the process returns to step S7. Note that in the case where the application software in FIG. 11 is executed proactively on the user terminal 20, in this step the control unit 21 calculates the departure time and determines whether the current time is the departure time.

If the result of the determination in step S11 is that it is time to depart, a departure alarm is notified to the subject (S13). As it is time to depart, the control unit 11 causes the UI unit 24 of the user terminal 20 to display a departure alarm. The departure alarm may be generated by a sound-generating member. When the departure alarm is notified, the examiner starts moving from his/her home or the like to the medical facility. Note that, if the application software in FIG. 11 is executed proactively on the user terminal 20, in this step, the control unit 21 may cause the UI unit 24 to notify the departure alarm.

Next, it is determined whether or not the subject has arrived at the hospital (S14a). Here, it is determined whether or not the subject has arrived at the destination hospital, etc., based on the detection result of the position detection unit of the user terminal 20. For this reason, the control unit 11 acquires information on the current location from the user terminal 20. If the result of this determination is that the subject has not arrived at the hospital, etc., the system goes into standby mode. Note that, when the application software in FIG. 11 is executed independently by the user terminal 20, in this step the control unit 21 makes the determination based on the detection result of the position detection unit in the user terminal 20.

If the result of the determination in step S14a is that the user has arrived at a hospital or the like, the process starts acquiring the estimated times of in-hospital tasks (S14b). In the hospital, there are in-hospital tasks such as pre-treatment (cleaning), examination, rest (recovery), explanation of examination results, and accounting (settlement). In this step, the control unit 11 acquires information regarding the estimated times of these tasks from the schedule management unit 33 in the in-hospital system 30. Note that, in the case where the application software in FIG. 11 is executed independently on the user terminal 20, in this step the control unit 21 acquires the information from the schedule management unit 33 in the in-hospital system 30 via the service server 10.

Next, the cleanliness level is checked (S14c). This cleanliness level check is performed in the same manner as step S5 in FIG. 2. The subject drinks the cleansing agent in a hospital or the like to thoroughly cleanse the intestines. At this time, the control unit 11 acquires an image of the subject's defecation by the photographing unit 23 through the user terminal 20, and transmits it to the service server 10 through the communication unit 22. The cleanliness level determination unit 14 in the service server 10 determines the cleanliness level based on the defecation image of the subject, and determines whether or not a sufficient cleanliness level has been reached. The operation of this cleanliness level check is the same as the flow of FIG. 3A or FIG. 3B described above, so a detailed description will be omitted. Also, during cleaning, the UI unit 24 of the user terminal 20 displays an advice display such as that shown in FIG. 9B described above. Note that, when the application software of FIG. 11 is executed independently on the user terminal 20, in this step, the control unit 21 cooperates with the cleanliness level determination unit 14 in the service server 10 to determine the cleanliness level.

Once the cleanliness has been checked, the estimated time for the entire inspection process to be completed is then displayed (S19). The control unit 11 estimates the estimated time for all inspection processes while checking the progress of the inspection and the schedule in the schedule management unit 33, and displays this estimated time on the UI unit 24 of the user terminal 20. This display will be described later with reference to FIG. 12. Note that, in the case where the application software in FIG. 11 is executed independently on the user terminal 20, in this step the control unit 21 works with the service server 10, schedule management unit 33, etc. to estimate the estimated time for all inspection processes, and displays this estimated time on the UI unit 24.

Next, it is determined whether the subject has left the hospital or the like (S21). When all tasks in the hospital, such as intestinal cleansing, examination, recovery, explanation of the examination results, and accounting (settlement), the subject leaves the hospital or the like. Here, the control unit 11 instructs the service server 10 to transmit information regarding the subject's current location acquired by a position detection unit such as GPS in the user terminal 20, acquires the subject's current location, and makes a judgment. In addition to the detection result by the position detection unit in the user terminal 20 of the subject, if the subject's location information can be acquired by the waiting room information acquisition unit 37, examination room information acquisition unit 38, and recovery room information acquisition unit 39 in the in-hospital system 34, this information may be used. If the result of the judgment in step S21 is that the subject has not left the hospital or the like, the process returns to step 19 and the above-mentioned operation is repeated. Instead of returning to step S19, the process may return to step S14c and check the cleansing degree. In this case, if the cleansing has already been completed, step S14c may be skipped. In addition, when the application software in Figure 11 is executed independently on the user terminal 20, in this step, the control unit 21 obtains current location information using the location detection unit in the user terminal 20 or information from the in-hospital system 34 and makes a judgment.

If the result of the determination in step S21 is that the subject has left the hospital, acquisition of the predicted times for the in-hospital tasks is terminated (S23). The acquisition of the predicted times for the in-hospital tasks was started in step S14b and was displayed on the UI unit 24 of the user terminal 20 (see FIG. 12). As a result of the determination in step S21, the subject has completed all tasks for the examination, so acquisition of the predicted times is terminated and the main operation flow is terminated.

Next, the display of the predicted time of completion of the entire examination process will be described with reference to FIG. 12. This display is performed in step S19 of FIG. 11. In the second embodiment, as in the first embodiment, the time prediction unit 15 predicts the start time of the in-hospital task, etc. (see S14b, S23 in FIG. 11). Based on the start time of the in-hospital task, etc., the in-hospital schedule can be displayed on the UI unit 24 of the user terminal 20. FIG. 12 is an example of displaying the in-hospital task schedule, similar to screen D8 in FIG. 9C, where CT indicates the current time. Note that, unlike screen D8 in FIG. 9C, in the second embodiment, cleaning is being performed at a medical facility. In this way, the in-hospital task schedule is displayed on the UI unit 24 of the user terminal 20, so that the subject can easily know what to do next and what time it will be completed.

As described above, in the second embodiment of the present invention, after the subject moves to a medical facility, he or she takes a cleaning agent to cleanse the intestines and undergoes an endoscopic examination. When undergoing this examination, status information such as the cleaning state of the subject during the endoscopic examination (see S14c in FIG. 11, S43, S45 in FIG. 3A and FIG. 3B, etc.) is obtained, and the progress of the examination is estimated based on this status information (see, for example, S11, S14a in FIG. 11, S49 in FIG. 3A and FIG. 3B, etc.), and advice is provided based on this progress (see S13 in FIG. 11, S49 in FIG. 3A and FIG. 3B, etc.). Therefore, the subject can receive the advice necessary to properly undergo the endoscopic examination, and can undergo the examination with peace of mind.

As described above, in each embodiment of the present invention, when an endoscopic examination is performed, status information such as the cleaning status of the subject of the endoscopic examination (see S5 in FIG. 2, S43, S45 in FIGS. 3A and 3B, S14c in FIG. 11, etc.) is obtained, and the progress of the examination is estimated based on this status information (see, for example, S5 in FIG. 2, S14a in FIG. 11, S49 in FIGS. 3A and 3B, etc.), and advice is provided based on this progress (see S49 in FIGS. 3A and 3B, etc.). This allows the subject to receive the advice necessary to properly undergo the endoscopic examination, allowing them to undergo the examination with peace of mind.

In addition, in each embodiment of the present invention, an examination guide method is provided that estimates the progress of a process related to an endoscopic examination of an examinee based on the condition information of the examinee and displays information related to the progress. Also, an examination guide method is provided that estimates the progress of a process related to an endoscopic examination of an examinee based on the condition information of the examinee and displays information related to the estimated progress. Also, a prediction method is provided in which the same thing (excrement of the same examinee, water containing excrement, etc.) is repeatedly acquired, and based on the temporal change in the transparency (cleanliness) of the acquired thing, the time until the same thing reaches a specific transparency is predicted. According to this prediction method, the end time of cleaning can be determined by multiple measurements (analysis of the image capture results, etc.) spanning a specific time in the first half of the start of cleaning. The end time of cleaning may be determined by referring to a specific cleaning progress model, or may be determined by multiple patterns, such as a first model if the change is fast and a second model if the change is slow, based on multiple measurements.

In this specification, we have mainly described examples using images of excrement, etc., but it is also possible to use optical sensors to see transmission, reflection, etc. instead of images. Instead of images, the pH of the water in the flush toilet, resistance, or reagent reaction may be used. This is a method to find out what percentage of the excrement is made up of the cleaning fluid, and it can be used because the cleaning fluid is a chemical that contains electrolytes such as sodium. In addition, in the case of a flush toilet, it is assumed that not only the excrement is photographed, but also the water that has accumulated in the flush toilet bowl is mixed in. This is also true for photographic images, and it is better to photograph the water in the flush toilet containing excrement and eliminate the influence of the original flush water on its transparency and cleanliness, but this can be considered to be within the margin of error.

In addition, in each embodiment of the present invention, when undergoing an endoscopic examination, status information such as the cleaning status of the subject of the endoscopic examination (see S5 in FIG. 2, S43 and S45 in FIGS. 3A and 3B, S14c in FIG. 11, etc.) is obtained, and the usage status (usage status of the area where at least one of the actions related to waiting, pre-treatment, examination, recovery, and payment is performed) at the medical facility where the subject undergoes the endoscopic examination is obtained (for example, S15 and S17 in FIG. 2), and the time related to the endoscopic examination is estimated based on the cleaning status information and the usage status information, and advice related to the clinical examination is displayed based on this estimation (for example, see S19 in FIG. 2). Therefore, since the individual times of the endoscopic examination consisting of each action can be predicted, the subject can easily make plans and can undergo the examination with peace of mind.

In addition, the examination guide method in each embodiment of the present invention estimates the progress of the process related to the preparation for the endoscopy examination of the examinee based on the change over time in the condition information of the examinee for the endoscopy examination (for example, see S37 to S43 in Figures 3A and 3B), and generates information on the end timing of the preparation process based on this estimated progress (for example, see S49 in Figures 3A and 3B). Therefore, according to the examination guide method in this embodiment, it is possible to receive the necessary advice to properly undergo an examination involving medical procedures such as an endoscopy examination or a clinical examination. In other words, based on the change in the condition information of the examinee, the progress of the preparation process for the examination (for example, the progress of the degree of cleansing when an intestinal cleansing is performed) is determined, and advice on the timing of the end of the preparation process is received based on the result of this determination.

In addition, in the examination guidance method in each embodiment of the present invention, the degree of progress of the preparation process is the degree of progress of the intestinal cleansing state of the endoscopy examination patient (e.g., FIG. 4), and the timing at which it is predicted that the intestinal cleansing state will reach a certain degree is set as the timing at which the preparation process will end (e.g., see S43 and S49 in FIG. 3A and FIG. 3B, the examination possible level Lev in FIG. 4, and FIG. 7 and FIG. 8). That is, in each embodiment, the timing at which the preparation will end is predicted based on the degree of progress of the intestinal cleansing state, and the examinee can receive appropriate advice regarding the timing at which to end the preparation.

In addition, in the examination guide method in each embodiment of the present invention, the change in state information over time is the change in the excretion image over time (see, for example, FIG. 5 and FIG. 6(b)), and based on this change in the excretion image over time, the change in the cleanliness of the intestine over time from the time the excretion image was captured is estimated (see, for example, FIG. 4 and FIG. 7(b)), and the timing at which the estimated change in cleanliness over time reaches a certain level is set as the end timing of the intestine cleansing state (see, for example, S49 in FIG. 3A and FIG. 3B, the testable level Lev in FIG. 4, FIG. 7, FIG. 8, etc.). That is, in each embodiment, the cleansing state of the intestine cleansing is repeatedly acquired (see, for example, S37 to S41 in FIG. 3A and FIG. 3B), and based on the change in the cleansing state over time, the future cleanliness is predicted (see, for example, S43 in FIG. 3A and FIG. 3B, the testable level Lev in FIG. 4), and the timing at which this predicted cleanliness reaches a testable level is set as the end timing of the cleansing (see, for example, S49 in FIG. 3A and FIG. 3B). This allows the patient to receive appropriate advice regarding the timing for completing preparations.

In addition, in the examination guidance method in each embodiment of the present invention, the time from the start of intestinal cleansing of the patient undergoing endoscopy is further taken into consideration to estimate the change in cleansing level over time (see, for example, S3 and S5 in FIG. 2, screen D8 in FIG. 9C, S19 in FIG. 11, FIG. 12, etc.). This allows the patient to receive advice on the appropriate timing to stop taking the cleansing agent after starting it.

In addition, in the examination guidance method in each embodiment of the present invention, the amount of intestinal cleansing agent taken by the endoscopy patient is estimated, and this estimated amount (which is roughly proportional to the time since the start of cleansing) is further taken into consideration to estimate the change in cleansing level over time. For example, in the example shown in Figures 3A and 3B, the amount of cleansing agent is displayed in step S25 (e.g., screens D2a to D2c in Figure 9B), and the patient can estimate the future change in cleansing level over time based on this amount of cleansing agent and the number of times the patient has taken it.

In addition, in the examination guidance method in each embodiment of the present invention, a guide is displayed on how the patient undergoing an endoscopy examination should take the medicine for intestinal cleansing (see, for example, S49 in Figs. 3A and 3B, and screens D2a to D2c in Fig. 9B, etc.). Patients often do not know how to take the medicine for intestinal cleansing, and being shown how to take it enables them to undergo the examination appropriately.

In addition, in the examination guidance method in each embodiment of the present invention, the guide display is changed depending on the medication that the patient takes for intestinal cleansing (see, for example, screens D2a to D2c in FIG. 9B). Because the guide display is switched depending on the medication, it is possible to prevent the patient from taking the medication in the wrong way.

In addition, in the examination guidance method in each embodiment of the present invention, the time to take the medicine, the amount of the medicine, or the amount of other medicine that should be taken together with the medicine is displayed as a guide on how to take the medicine (see, for example, screens D2a to D2c in FIG. 9B). Specifically, the time to take the medicine, the amount to take, etc. are displayed, so that it is possible to prevent the examinee from taking the medicine in the wrong way.

In addition, in the examination guide method in each embodiment of the present invention, the excretion image in which the state in which the endoscopy examinee has excreted and the multiple excretion images are captured are compared with the excretion progress model in terms of features to estimate the time change in cleansing level and estimate the end timing of the intestinal cleansing state (see, for example, S39 to S43 in Figures 3A and 3B, Figure 5, etc.). That is, the excretion image of the examinee is compared with the excretion progress model (see, for example, the reference image sequence in Figure 5), and the cleansing level is estimated using the features of the excretion image of the examinee and the features of the excretion progress model. In addition, since an excretion image is captured each time the examinee defecates (see, for example, S37 in Figures 3A and 3B, screen D4 in Figure 9B, etc.), the cleansing level can be obtained for multiple excretion images. Based on the time change in the multiple cleansing levels, the end timing of the intestinal cleansing can be predicted.

In addition, in the examination guide method in each embodiment of the present invention, the end timing of the intestinal cleaning state is estimated using an AI model trained using an excretion image in which the excretion state is captured, and multiple excretion images in which the state in which the endoscopy examinee excretes is captured (see, for example, S45, S49 in Figures 3A and 3B, Figures 6, 8, etc.). In addition, in the examination guide method in each embodiment of the present invention, an excretion image in which the subject excretes is input to an AI model trained using at least one of the time until completion of cleaning and the amount of cleaning agent in addition to the excretion image in which the excretion state is captured, and the end timing of the intestinal cleaning state is estimated (see, for example, Figures 6 and 8). An AI model is generated using big data, etc., and the end timing of the intestinal cleaning state can be estimated using this AI model.

In addition, in the examination guide method in each embodiment of the present invention, an AI model trained using multiple learning data sets in which images of the excretion state of an endoscopy patient are annotated with the degree of cleansing is input with images of the excretion state, and the degree of cleansing is estimated, and the timing of the end of the intestinal cleansing state is estimated (see, for example, Figures 6 and 8). By using the AI model, it is possible to make inferences using big data, etc.

In addition, in the examination guide method in each embodiment of the present invention, the washing end timing is estimated by inputting a plurality of images of the subject's excretion into an AI model trained using a plurality of learning data sets in which a plurality of images of the excretion are annotated with the washing time or the amount of detergent taken until the subject reaches an examination-ready state (see, for example, FIG. 8, etc.). Therefore, if big data is available, the washing end timing can be easily estimated.

In addition, in the examination guide method in each embodiment of the present invention, the end timing of the preparation process and the predicted examination start time determined by the examination time deviation situation of the examinee (including cleaning) before the endoscopic examination examinee are guided (for example, see S15 to S19 in FIG. 2, FIG. 9C, S19 in FIG. 11, FIG. 12, etc.). Since an endoscopic examination has various stages such as cleaning, examination, recovery (rest), result explanation, and payment, and moreover, since multiple examinees are undergoing examinations, if the previous examinee deviates from the schedule, the examination start time of the examinee who is receiving assistance for the examination will change. Even in this case, according to this embodiment, the change in the examination start time according to the deviation of the previous examinee is notified, which is convenient. In addition, in the examination guide method in each embodiment of the present invention, information regarding the end timing of the preparation process for the endoscopic examination is generated according to the examinee's meal history information. Since the difficulty of residue remaining varies depending on the examinee's meal history, information regarding the end timing of the preparation process can be generated with high accuracy by using this history information.

In addition, in each embodiment of the present invention, the second timing is estimated according to the treatment after the first timing. That is, since an endoscopic examination involves multiple steps of treatment, and the time required for each step affects the start timing of the next step, the next timing is estimated according to each individual timing. The end timing of cleaning may be estimated by appropriately combining the time from the start of cleaning, the amount of cleaning agent consumed, the progress of the cleaning state, classical methods or AI, classical methods and AI, etc.

In addition, in the examination guidance method in each embodiment of the present invention, the progress of the process related to the endoscopic examination is estimated based on the condition information of the patient undergoing the endoscopic examination and the congestion level or staff availability of the hospital where the endoscopic examination is performed, and a guide is displayed according to this estimated progress (see, for example, S15 to S19 in FIG. 2 and S19 in FIG. 11). That is, in each embodiment, the end time is estimated taking into account the in-hospital situation where the endoscopic examination is performed, and this time is displayed.

In the examination guide method in each embodiment of the present invention, the condition information of the patient undergoing an endoscopic examination is the progress of the process related to the endoscopic examination (actual value, how far it has progressed) and/or the risk indicating the possibility that the patient will undergo treatment, and the start time or end time of the endoscopic examination is estimated according to the operating status of the endoscopic examination room and/or the endoscopic examination staff. In the examination guide method in each embodiment of the present invention, if the endoscopic examination room and/or the endoscopic examination staff performing the endoscopic examination to be performed by the patient is used/engaged in a second endoscopic examination on another patient, and the second endoscopic examination deviates (shortens or extends) from the time originally expected (expected before the second endoscopic examination) due to insertion difficulty, lesion discovery, biopsy, treatment, normality, etc., the estimated start time or estimated end time of the endoscopic examination to be performed by the patient is changed (advanced or delayed). In the examination guide method according to each embodiment of the present invention, the congestion state of the hospital where the endoscopic examination is performed refers to the congestion state of any of waiting, pre-treatment, examination, recovery, notification of examination results, and accounting, or a state in which the time required to perform a process in that space deviates from the standard time (the standard time at that facility).

In addition, the examination guide device in each embodiment of the present invention has a status acquisition unit that acquires status information of the endoscopic examination patient, a progress estimation unit that estimates the progress of the examination preparation of the endoscopic examination patient based on the time change of the acquired status information, and an advice generation unit for displaying advice related to the examination preparation based on the estimated progress. In the endoscopic examination support system shown in Figures 1 and 10, the estimation of the progress of the examination preparation of the patient and the generation of advice based on this estimation are mainly performed in the service server 10. However, these processes are not limited to the service server 10, and may be performed in the user terminal 20 or the in-hospital system 30, 34. For this reason, it is sufficient if some of the functions in the service server 10 can be executed in the user terminal 20 or the in-hospital system 30, 34.

The above-mentioned preparation for the examination is preparation for digestive tract cleaning, and the status acquisition unit acquires information related to the excretion of the endoscopy patient (see, for example, S39, 41, etc. in Figs. 3A and 3B), and the progress estimation unit estimates the progress of the endoscopy patient regarding digestive tract cleaning as the progress status (see, for example, S43, etc. in Figs. 3A and 3B). The progress estimation unit also estimates the progress of digestive tract cleaning and the time required to reach a certain degree of cleaning or the number and amount of cleaning agent intake (see, for example, S49 in Figs. 3A and 3B, Figs. 4 to 8, etc.), and the advice generation unit generates information based on the estimation result of the progress estimation unit (see, for example, the instruction transmission unit 16 in Fig. 1, S49 in Figs. 3A and 3B, etc.).

In addition, the examination guide device in each embodiment of the present invention has a receiver request input unit that inputs the requests of the endoscopy examination patient, and the advice display unit displays advice taking into consideration the requests of the endoscopy examination patient. In addition, the examination guide method acquires condition information of the patient undergoing a clinical examination, estimates the progress of the patient's preparation for the examination based on temporal changes in the condition information, and displays advice related to preparation for the clinical examination based on the progress.

Furthermore, the examination guidance method in each embodiment of the present invention predicts (estimates) a first timing (e.g., start timing) (which also includes cleaning) and a second timing (e.g., end timing) that is determined according to treatment after the first timing, depending on the congestion situation in the hospital. In other words, in a hospital or the like where an examination is performed, there are multiple patients, and the congestion situation in the hospital is not constant. Therefore, when performing examination guidance, in each embodiment, the prediction (estimate) is made taking into account the congestion situation in the hospital.

In addition, the examination guide device in each embodiment of the present invention has a status acquisition unit that acquires status information of the patient undergoing the endoscopic examination (e.g., see S5 in FIG. 2, FIGS. 3A and 3B, S14a in FIG. 11, etc.), a usage status acquisition unit that acquires usage status at the medical institution where the patient undergoes the endoscopic examination (e.g., see S15, S17 in FIG. 2, etc.), and a time estimation unit that estimates the time related to the endoscopic examination based on the status information and usage status (e.g., see S19 in FIG. 2 and FIG. 11, screen D8 in FIG. 9C, FIG. 12, etc.). In addition, this examination guide device may have an advice display unit that displays advice related to the endoscopic examination based on the estimation. That is, this examination guide device estimates the time related to the examination, such as the start time and end time of the endoscopic examination, based on the endoscope used in the endoscopic examination and the usage status of the examination room. In addition, the above-mentioned usage situation is the usage situation in the space where at least one of the actions related to waiting, pre-treatment, examination, treatment, recovery, and payment is performed at a medical institution, and the time estimation unit predicts the start or end time of each action, and the advice display unit displays advice related to the start of travel to the medical institution, the start or end of each action, and the completion of all actions at the medical institution.

In addition, the examination guidance method in each embodiment of the present invention has a progress estimation unit (e.g., see S49 in Figures 3A and 3B) that acquires status information of a subject of a clinical examination and estimates the progress of the subject's examination preparation based on changes in this status information over time, and an advice generation unit (e.g., see S49 in Figures 3A and 3B) that displays advice regarding examination preparation based on the progress.

In addition, the endoscopic examination support method in each embodiment of the present invention acquires status information of a patient undergoing a clinical examination, acquires usage status at the medical institution where the patient undergoes the clinical examination, estimates a time related to the clinical examination based on the status information and usage status, and displays advice related to the clinical examination based on the estimation. Although each embodiment has been described primarily in the case of an endoscopic examination, the method can be applied to clinical examinations that involve various preparations and pre-processing before the examination, in addition to endoscopic examinations.

In each embodiment of the present invention, the service server 10 has been described as providing advice to the subject when undergoing an endoscopic examination, but the service may be provided by multiple servers, or may be provided only within the user terminal 20. In this case, the user terminal 20 may have functions such as a schedule management unit 13, a cleanliness assessment unit 14, and a time prediction unit 15, and may be linked to the in-hospital systems 30, 34. In addition, while the present embodiment has been described as being primarily about undergoing an endoscopic examination, various preparations and pre-processing are required when undergoing clinical examinations other than endoscopic examinations, and the present embodiment can be applied in such cases as well.

In addition, in each embodiment of the present invention, logic-based judgment has been mainly described, and judgment has been made partially by inference using machine learning. In this embodiment, whether to make a logic-based judgment or an inference judgment may be appropriately selected and used. Also, in the judgment process, a hybrid judgment may be made by partially utilizing the advantages of each.

In each embodiment of the present invention, the control units 11, 21, 31, and 35 have been described as devices that are configured with a CPU, memory, and the like. However, in addition to being configured in a software manner using a CPU and a program, some or all of the units may be configured with hardware circuits, or may be configured with hardware such as gate circuits generated based on a program language described in Verilog or VHDL (Verilog Hardware Description Language), or may be configured with hardware using software such as a DSP (Digital Signal Processor). Of course, these may be combined as appropriate.

In addition, the control units 11, 21, 31, and 35 are not limited to CPUs, and may be any element that functions as a controller, and the processing of each of the above-mentioned units may be performed by one or more processors configured as hardware. For example, each unit may be a processor configured as an electronic circuit, or each circuit unit in a processor configured as an integrated circuit such as an FPGA (Field Programmable Gate Array). Alternatively, a processor configured as one or more CPUs may execute the functions of each unit by reading and executing a computer program recorded on a recording medium.

In addition, in each embodiment of the present invention, the service server 10 has been described as having a control unit 11, a communication unit 12, a schedule management unit 13, a cleanliness assessment unit 14, a time prediction unit 15, an instruction transmission unit 16, and an examination result recording unit 17. However, these do not need to be provided in a single device, and the above-mentioned units may be distributed, for example, as long as they are connected by a communication network such as the Internet. Similarly, the user terminal 20 has been described as having a control unit 21, a communication unit 22, an imaging unit 23, and a UI unit 24. However, these do not need to be provided in a single device, and the above-mentioned units may be distributed, for example, as long as they are connected by a communication network such as the Internet. The same applies to the in-hospital systems 30, 34, etc.

In addition, in recent years, artificial intelligence capable of making judgments based on various criteria has come to be used in many cases, and it goes without saying that improvements such as performing each branch of the flowchart shown here all at once also fall within the scope of the present invention. If the user could input the pros and cons of such control, the user's preferences could be learned and the embodiment shown in this application could be customized in a direction appropriate to the user.

Furthermore, components from different embodiments may be combined as appropriate. In particular, operations utilizing biological reactions, including voice recognition, require appropriate sensors, interfaces, and determination circuits, which are not specifically described to avoid complication. However, it should be noted that the present invention can also be achieved by various improved and alternative technologies that can substitute for manual operations by the user.

In addition, among the technologies described in this specification, the controls mainly described in the flowcharts can often be set by a program, and may be stored on a recording medium or in a recording unit. The method of recording on this recording medium or recording unit may be recording at the time of product shipment, using a distributed recording medium, or downloading via the Internet.

In addition, in one embodiment of the present invention, the operation of this embodiment is explained using a flowchart, but the order of the processing steps may be changed, any steps may be omitted, steps may be added, and the specific processing content within each step may be changed.

Furthermore, even if the operational flow in the claims, specification, and drawings is described using words expressing order such as "first" and "next" for convenience, this does not mean that it is necessary to carry out the steps in this order unless specifically explained.

The present invention is not limited to the above-described embodiment, and in the implementation stage, the components can be modified to the extent that does not deviate from the gist of the invention. In addition, various inventions can be formed by appropriate combinations of multiple components disclosed in the above-described embodiment. For example, some of the components shown in the embodiment may be deleted. Furthermore, components from different embodiments may be appropriately combined.

10: Service server, 11: Control unit, 12: Communication unit, 13: Schedule management unit, 14: Cleanliness assessment unit, 15: Time prediction unit, 16: Instruction transmission unit, 17: Examination result recording unit, 20: User terminal, 21: Control unit, 22: Communication unit, 23: Imaging unit, 24: UI unit, 30: In-hospital system, 31: Control unit, 32: Communication unit, 33: Schedule management unit, 34: In-hospital system, 35: Control unit, 36: Communication unit, 37: Waiting room information acquisition unit, 38: Examination room information acquisition unit, 39: Recovery room information acquisition unit, 40: Pre-treatment room information acquisition unit

Claims (9)

A step of automatically estimating a progress level of a process related to preparation for an endoscopic examination of the endoscopic examination patient by a computer based on a temporal change in condition information of the endoscopic examination patient;
A computer-based inspection guidance method, comprising: a step of automatically generating information regarding an end timing of a preparation process based on the estimated progress degree,
The change in the state information over time is a change in the excretion image over time,
A computer is used to estimate a time change in the cleansing degree of the intestine in the future from the time when the excretion image was captured based on the time change in the excretion image;
The timing when the estimated change in cleansing degree reaches a certain level is determined by the computer as the end timing of the intestinal cleansing state.
A computer-aided inspection guiding method comprising: A step of automatically estimating a progress level of a process related to preparation for an endoscopic examination of the endoscopic examination patient by a computer based on a temporal change in condition information of the endoscopic examination patient;
A computer-based inspection guidance method, comprising : automatically generating information regarding an end timing of a preparation process based on the estimated progress level,
The degree of progress of the above preparation process is the degree of progress of the intestinal cleansing state of the endoscopy examinee,
The timing when the intestinal cleaning state reaches a certain degree is determined as the end timing of the preparation process;
An excretion image obtained by photographing the state in which the endoscopy examinee excretes; and
A computer is used to compare the features of the plurality of excretion images with an excretion progress model to estimate a change in cleansing degree over time, and to estimate an end timing of the intestinal cleansing state.
A computer-aided inspection guiding method comprising: 3. The method for guiding examination by a computer according to claim 2, further comprising the step of: taking into consideration the time from the start of intestinal cleansing of the patient undergoing the endoscopy, and estimating the change in cleansing degree over time by the computer . 3. The computer-based examination guidance method according to claim 2, further comprising the steps of: estimating the amount of intestinal cleansing agent taken by the patient undergoing the endoscopy examination using a computer; and further taking into account this estimated amount of intestinal cleansing agent , thereby estimating the change in cleansing level over time. The computer-based examination guidance method according to claim 2, characterized in that the end timing of the intestinal cleansing state is estimated by a computer using an AI model trained using excretion images of the patient excreting, and a plurality of excretion images of the patient excreting. The computer-based examination guidance method according to claim 2, characterized in that an AI model is trained using images of the subject excreting, as well as images of the subject's excretion, and at least one of the time until completion of cleaning and the amount of cleaning agent, and an image of the subject excreting is input to the AI model, and the computer estimates the timing of the end of the intestinal cleaning state. The computer-based examination guidance method according to claim 2, characterized in that an AI model is trained using multiple learning data sets in which images of the excretion state of the patient undergoing an endoscopic examination are input into the AI model, and the AI model estimates the cleansing level by computer , thereby estimating the timing at which the intestinal cleansing state will end. The computer-based inspection guidance method according to claim 2, characterized in that a plurality of images of the subject's excretion state are input to an AI model trained using a plurality of learning data sets in which a plurality of images of the excretion state are annotated with the cleaning time or the amount of detergent ingested until the subject reaches a state where the examination can be performed, and the computer estimates the timing for the end of cleaning. A status acquisition unit that acquires status information of an endoscopy patient;
a progress estimation unit that estimates a progress status of the examination preparation of the endoscopy examination subject based on a temporal change in the state information;
an advice generating unit for displaying advice regarding the preparation for the examination based on the progress status;
having
The progress estimation unit estimates the progress of the digestive tract cleansing and the time required to reach a certain degree of cleansing or the number and amount of cleaning agent intake,
the advice generation unit generates information based on an estimation result of the progress estimation unit;
An inspection guide device comprising:

Images