Processing apparatus, processing program, processing method and processing system

Abstract

To provide a processing apparatus, a processing program, a processing method and a processing system capable of evaluating a state of a subject more appropriately.SOLUTION: Measurement information obtained by measuring at least a part of a body of a subject by a measurement apparatus is received. First evaluation information indicating a result of evaluating a state of the subject is obtained by inputting the measurement information into a trained evaluation model for evaluating the state of the subject. The first evaluation information is transmitted to an expert terminal apparatus usable by one or more first experts who are permitted to access the first evaluation information in advance by an organization. Second evaluation information indicating a result of evaluating the first evaluation information, which is inputted by the one or more first experts, is received from the expert terminal apparatus. Evaluation result information related to a result of evaluating the state is generated based on the second evaluation information.SELECTED DRAWING: Figure 7A

Description

[Technical Field]

[0001] The present disclosure relates to a processing device, a processing program, a processing method, and a processing system capable of processing measurement information of a subject's body. [Background technology]

[0002] Processing systems for evaluating the condition of a subject have been known for some time. For example, Patent Document 1 describes a system for executing "an oral care management method, which comprises creating user information and client information for a person requiring oral care, conducting an assessment on the person requiring oral care to create assessment information, creating an oral care assessment, an oral care plan, and a denture creation plan based on the assessment information, inputting and registering these into a computer via a program, and managing oral care using this information." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-167215 Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, in light of the above-described technology, the present disclosure aims to provide a processing device, processing program, processing method, and processing system that can more appropriately evaluate the condition of a subject through various embodiments. [Means for solving the problem]

[0005] According to one aspect of the present disclosure, there is provided a processing device having at least one processor, with access restricted to those other than those previously authorized by an organization, wherein the at least one processor is configured to: receive measurement information obtained by measuring at least a portion of a subject's body using a measurement device; input the measurement information into a learned evaluation model for evaluating the subject's condition, thereby obtaining first evaluation information indicating the results of evaluating the subject's condition; transmit the first evaluation information to an expert terminal device usable by one or more first experts who have been previously authorized by the organization to access the first evaluation information; receive from the expert terminal device second evaluation information indicating the results of evaluating the first evaluation information input by the one or more first experts; and perform processing to generate evaluation result information related to the results of evaluating the condition based on the second evaluation information.

[0006] According to one aspect of the present disclosure, there is provided a processing program that, when executed by at least one processor provided in a computer to which access is restricted to those other than those previously authorized by an organization, causes the at least one processor to function in the following manner: receive measurement information obtained by measuring at least a portion of a subject's body using a measurement device; input the measurement information into a learned evaluation model for evaluating the subject's condition, thereby obtaining first evaluation information indicating the results of evaluating the subject's condition; transmit the first evaluation information to an expert terminal device that can be used by one or more first experts who have been previously authorized by the organization to access the first evaluation information; receive from the expert terminal device second evaluation information input by the one or more first experts indicating the results of evaluating the first evaluation information; and generate evaluation result information related to the results of evaluating the condition based on the second evaluation information.

[0007] According to one aspect of the present disclosure, there is provided a processing method executed by at least one processor provided in a computer to which access is restricted to those other than those previously authorized by an organization, the processing method including the steps of: receiving measurement information of at least a part of a subject's body measured by a measurement device; obtaining first evaluation information indicating a result of evaluation of the subject's condition by inputting the measurement information into a learned evaluation model for evaluating the subject's condition; transmitting the first evaluation information to an expert terminal device usable by one or more first experts who have been previously authorized by the organization to access the first evaluation information; receiving from the expert terminal device second evaluation information indicating a result of evaluation of the first evaluation information input by the one or more first experts; and generating evaluation result information related to the result of evaluation of the condition based on the second evaluation information.

[0008] According to one aspect of the present disclosure, there is provided a processing system including a measuring device configured to measure measurement information of at least a part of a subject's body, and a processing device according to claim 1 that is communicatively connected to the measuring device. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to provide a processing device, a processing program, a processing method, and a processing system that are capable of more appropriately evaluating the condition of a subject.

[0010] It should be noted that the above effects are merely illustrative for the sake of convenience and are not limiting. In addition to or instead of the above effects, any effect described in this disclosure or an effect obvious to a person skilled in the art may be achieved. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. [Figure 2A]FIG. 2A is a block diagram showing a configuration of a processing device 100 according to an embodiment of the present disclosure. [Figure 2B] FIG. 2B is a block diagram showing a configuration of a terminal device 200 according to an embodiment of the present disclosure. [Figure 3A] FIG. 3A is a diagram conceptually showing a subject management table stored in processing device 100 according to an embodiment of the present disclosure. [Figure 3B] FIG. 3B is a diagram conceptually illustrating an example of a subject image according to an embodiment of the present disclosure. [Figure 3C] FIG. 3C is a diagram conceptually illustrating an expert management table stored in the processing device 100 according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a diagram showing a processing sequence executed by the processing system 1 according to an embodiment of the present disclosure. [Figure 5A] FIG. 5A is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 5B] FIG. 5B is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a diagram showing a processing flow for generating a trained evaluation model according to an embodiment of the present disclosure. [Figure 7A] FIG. 7A is a diagram showing an example of a first evaluation information screen output on the expert terminal device 200-2 according to an embodiment of the present disclosure. [Figure 7B] FIG. 7B is a diagram showing an example of a second evaluation information screen output on the expert terminal device 200-2 according to an embodiment of the present disclosure. [Figure 7C] FIG. 7C is a diagram showing an example of an evaluation result information screen output in the measurement device 200-1 according to an embodiment of the present disclosure. [Figure 7D] FIG. 7D is a diagram showing an example of an evaluation result information screen output in the measurement device 200-1 according to an embodiment of the present disclosure. [Figure 8] FIG. 8 is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] 1. Overview of Processing System 1 A processing system 1 according to the present disclosure includes a processing device to which access is restricted to those other than those previously authorized by an organization. The processing system 1 uses the processing device to acquire measurement information by measuring at least a part of a subject's body with a measurement device, and acquires first evaluation information by inputting the measurement information into a trained evaluation model. The processing system 1 also uses the processing device to transmit the first evaluation information to one or more experts previously authorized by the organization to access the first evaluation information. The processing system 1 also uses the processing device to acquire second evaluation information, which is a result of evaluating the first evaluation information, from the first experts previously authorized by the organization to access the first evaluation information. The processing system 1 also generates evaluation result information based on the acquired second evaluation information and outputs the information to, for example, a user terminal device usable by the subject or user.

[0013] As an example, such a processing system 1 uses a subject image of the subject's oral cavity as measurement information, and acquires first evaluation information, which is an evaluation result of the subject's morbidity for a disease exhibiting oral cavity findings, based on the subject image. Then, a first expert, who has been granted access to the first evaluation information in advance by the organization, evaluates the acquired first evaluation information and, if necessary, modifies it (the modification may be any of replacing content, adding content, deleting content, etc.) to generate second evaluation information. Furthermore, upon receiving second evaluation information, which is the result of the first expert's evaluation of the first evaluation information, the processing system 1 generates evaluation result information related to the result of the evaluation of the condition based on the second evaluation information. Then, the processing system 1 reports the evaluation result information to the subject or user.

[0014] Therefore, when evaluating the subject's condition (e.g., whether the subject is suffering from a disease that has oral cavity symptoms), the processing system 1 can provide the subject or user with more accurate evaluation result information by using second evaluation information evaluated by an expert.

[0015] Furthermore, the processing system 1 selects experts who will evaluate the first evaluation information and generate the second evaluation information from among experts who have been granted access to the first evaluation information in advance by the organization that manages the processing device, based on attribute information of each expert. That is, the processing system 1 is capable of matching experts who will evaluate the first evaluation information. Therefore, the processing system 1 can match the experts who are best suited for the evaluation and generate the second evaluation information more appropriately.

[0016] Furthermore, the processing system 1 selects an expert who generates the second evaluation information from among experts who have been granted access to the first evaluation information in advance by the organization that manages the processing device. This allows access to the first evaluation information only from within the organization, and restricts access from outside the organization. On the other hand, evaluation result information generated based on the second evaluation information can be provided to users or subjects outside the organization. In this way, by managing access according to the attributes of the information users, it is possible to appropriately manage the distribution of processing devices.

[0017] Furthermore, the processing system 1 is expected to report the evaluation result information to the subject, who will then receive diagnosis and care from a specialist to improve or maintain their condition. The processing system 1 is capable of matching such specialists. Therefore, the processing system 1 can introduce the subject to a more appropriate specialist.

[0018] Furthermore, the processing system 1 provides the subject with evaluation result information that includes medical advice according to the subject's physical and mental condition if the specialist introduced is a doctor or dentist, and includes a recommendation to visit a medical institution to which the specialist belongs if the specialist introduced is not a doctor or dentist. This allows for smoother operation than providing evaluation result information that includes a medical opinion such as a diagnosis.

[0019] In the present disclosure, the term "subject" refers to anyone who is the subject of measurement information, including patients, test subjects, evaluation subjects, healthy individuals, healthy individuals, and individuals requiring care. In the present disclosure, the term "user" refers to anyone who can use a measurement device used to measure measurement information, including the subject themselves, their guardians, caregivers, service providers, superiors, subordinates, colleagues, teachers, care managers and administrators, and experts themselves. In the present disclosure, the term "expert" is merely a term used to distinguish the subject from the user and does not necessarily require a high level of expertise, license, title, or qualification. In other words, the term "expert" refers to anyone who can evaluate the first evaluation information, which is the result of evaluating the subject's condition based on the measurement information. This term includes medical professionals such as dentists, doctors, dental hygienists, nurses, clinical laboratory technicians, registered dietitians, physical therapists, occupational therapists, dental technicians, emergency medical technicians, and speech-language-hearing therapists, as well as caregivers, care workers, and acupuncturists. It is desirable that the subject, the user, and the expert (particularly the first expert) belong to the same organization.

[0020] Furthermore, as mentioned above, the subject, user, and expert are merely names given to distinguish one from another based on the roles and situations in which they are involved in the processing system 1. Therefore, it is natural that the subject, user, and expert may each be the same person. In addition, the subject, user, and expert may each refer to any individual, or may each refer to any organization to which multiple subjects, users, and experts belong (for example, a medical institution, care facility, or business to which each of the subjects, users, and experts belong).

[0021] Similarly, the terms measuring device, user terminal device, and expert terminal device are merely names given to distinguish one from another based on the attributes of the users in the processing system 1. Therefore, it goes without saying that a measuring device may function as a user terminal device or an expert terminal device, an expert terminal device may function as a measuring device or a user terminal device, and a user terminal device may function as a measuring device or an expert terminal device.

[0022] Furthermore, in the present disclosure, the organization may be any organization to which subjects, users, experts, etc. can belong. Organizations can be classified into various categories, such as public organizations such as government agencies and local governments, and private organizations with profit-making or non-profit purposes, and any of these may be used. Examples of such organizations include medical institutions, companies, schools, clubs, local communities, and households. Among these organizations, it is desirable that such organizations be able to restrict access to the processing device to those other than those authorized in advance, i.e., to restrict access to the processing device. In particular, it is desirable that the organization be able to manage the experts who have access to the first evaluation information to only authorized experts in advance. Examples of such management include a method of registering experts in advance for use with services provided by the processing system, or a method of managing the experts after entering into a contractual relationship with the experts, such as an employment contract or a service outsourcing contract.

[0023] Furthermore, in this disclosure, terms such as "first" and "second" may be used, but these do not necessarily specify a specific order or number, and are merely used to distinguish between connected words. Therefore, in addition to "first" and "second," "third," "fourth," "fifth," etc. may also be connected. Furthermore, the words connected with "first" and "second" do not have to be singular and may naturally be plural. For example, although there are descriptions such as "first evaluation information" and "second evaluation information," there may be multiple pieces of first evaluation information, and there may also be multiple pieces of second evaluation information.

[0024] In the present disclosure, the "condition of the subject" may be any information indicating the mental and physical condition of the subject. Examples of such conditions include conditions that can be evaluated using measurement information, preferably abnormalities or changes occurring in the subject's mind and body, more preferably the subject's mental and physical functions, physical condition, health condition, and combinations thereof, and even more preferably the condition of the subject's oral cavity, the subject's illness resulting from a disease that is found in the oral cavity, and combinations thereof.

[0025] Examples of the state of the subject's oral cavity include the state of at least one of the lips, sublingual, gums or oral mucosa, saliva, remaining teeth, dentures, oral hygiene, and toothache, as well as a comprehensive evaluation of these. In particular, the lips, sublingual, gums or oral mucosa, saliva, remaining teeth, dentures, oral hygiene, and toothache are states shown on an assessment sheet called the Oral Health Assessment Tool (OHAT), and are preferred as indicators for evaluating the state of the oral cavity.

[0026] Examples of diseases that exhibit findings in the oral cavity of a subject include infectious diseases such as influenza, coronavirus infection, streptococcal infection, adenovirus infection, EB virus infection, mycoplasma infection, hand, foot, and mouth disease, herpangina, candidiasis, and other infections; diseases presenting with vascular disorders or mucosal disorders such as arteriosclerosis, diabetes, and hypertension; tumors such as tongue cancer and pharyngeal cancer; periodontal diseases such as dental caries, gingivitis, and periodontal disease; and combinations thereof. In particular, among these diseases, the treatment system 1 can be suitably applied to influenza, which has a unique pattern in lymphoid follicles that appear in the deepest part of the pharynx located in the oral cavity.

[0027] Other examples of the "subject's condition" include glaucoma, diabetic retinopathy, age-related macular degeneration, fundus hemorrhage, retinal edema, optic disc abnormalities, arrhythmia (paroxysmal supraventricular tachycardia, atrial fibrillation, etc.), ischemic heart disease (angina pectoris, etc.), tachycardia, bradycardia, heart rate fluctuations, abnormal waveforms (ST changes, abnormal Q waves, etc.), hypertension, high blood pressure, low blood pressure, valvular heart disease, atrial septal defect, pneumonia, bronchitis, abnormal respiratory sounds, abnormal heart sounds, and heart murmurs.

[0028] In the following, the condition of the subject will be described using as examples the condition of the oral cavity and the presence or absence of influenza-related diseases, but of course, the condition is not limited to these.

[0029] Furthermore, the indicator indicating the state of the subject may be any information indicating a specific value, classification, category, etc. For example, examples of indicators indicating a classification or category include classifications such as "negative," "positive," and "suspected positive" according to any evaluation standard, numerical values ​​of "0 points," "1 point," and "2 points," classifications such as "healthy," "slightly poor," and "pathological," a specific score, the degree of risk for a risk factor, and combinations thereof.

[0030] Furthermore, in this disclosure, "evaluation" broadly includes evaluations related to the condition of a subject, and therefore includes various evaluations, such as a definitive diagnosis by a dentist, a doctor, or the like, an evaluation other than a definitive diagnosis by one of the experts listed above, an evaluation to assist a definitive diagnosis, an evaluation by a trained evaluation model, an evaluation of the evaluation results by a trained evaluation model or another expert, or the like (including confirmation or correction of the content), and combinations of these.

[0031] 2. Configuration of Processing System 1 1 is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. According to Fig. 1, the processing system 1 includes a processing device 100, a measuring device 200-1, and an expert terminal device 200-2, and each device is connected to each other so as to be able to communicate with each other via a wired or wireless network.

[0032] In the present disclosure, the processing device 100 may be any device capable of performing the processing executed by the processing device 100. That is, various devices, such as an on-premise server device, a cloud server device, a smartphone, a tablet device, a laptop PC, and a desktop PC, can be used as the processing device. In the present disclosure, the processing device 100 is preferably managed by a specific organization (organization A in FIG. 1). However, this management does not mean that the processing device 100 is a device installed by the organization or located in the organization's facilities. For example, it is sufficient if the organization manages who can access various information generated by or received by the processing device 100, such as a cloud server device provided by another organization with which the organization has a contract, and can restrict access by anyone other than those authorized in advance.

[0033] Also, any of the terminal devices 200, such as the measuring device 200-1 or the expert terminal device 200-2, can function as a processing device. Furthermore, in the present disclosure, the storage and processing performed by the processing device 100 may be distributed to other terminal devices, other server devices, etc. In other words, the processing device 100 is not limited to being configured in a single housing, but also includes a combination of the various devices exemplified above.

[0034] Furthermore, in the present disclosure, measurement device 200-1 may be any device capable of measuring the subject's body. That is, although not limited to the following devices, measurement device 200-1 is preferably a device for measuring medical information about the subject's body, and more preferably includes various devices such as a smartphone, tablet device, laptop PC, desktop PC, medical oral cavity imaging device, endoscopic device, ophthalmologic imaging device, CT device, MRI device, Holter electrocardiogram device, wristwatch-type electrocardiogram device, ultrasound device, electronic stethoscope device, and X-ray device. In the following, a case where a medical oral cavity imaging device is used as an example of measurement device 200-1 will be described, but of course measurement devices are not limited to this.

[0035] Furthermore, in the present disclosure, the expert terminal device 200-2 may be any device that inputs various information and transmits and receives this information. Preferably, such an expert terminal device 200-2 is managed by the organization that manages the processing device 100 (organization A in FIG. 1). However, this management does not mean that the device is purchased by the organization or that the device is only available at the organization's facilities. Any terminal device may be used by an expert who is permitted by the organization to access the processing device 100 or information generated or stored by the processing device 100. Examples of such an expert terminal device 200-2 include, but are not limited to, a smartphone, a tablet device, a laptop PC, and a desktop PC.

[0036] 1, the processing system 1 may also include one or more user terminal devices that can be used by a user or a subject. In addition, in this disclosure, the measurement device 200-1, the expert terminal device 200-2, and the user terminal devices may be collectively referred to as the terminal devices 200.

[0037] 1 shows only one each of the processing device 100, the measuring device 200-1, and the expert terminal device 200-2, but in the present disclosure, multiple units of each of these devices may be included. For example, multiple users may each own a measuring device 200-1, and each measuring device 200-1 may be included in the processing system 1. Similarly, multiple experts may each own an expert terminal device 200-2, and each expert terminal device 200-2 may be included in the processing system 1.

[0038] FIG. 2A is a block diagram showing the configuration of a processing device 100 according to an embodiment of the present disclosure. According to FIG. 2A, the processing device 100 includes a processor 111, a memory 112, and a communication interface 113. These components are electrically connected to one another via control lines and data lines. The processing device 100 does not need to include all of the components shown in FIG. 2A; some components may be omitted, or other components may be added. For example, an external memory, a database device, a server device, or the like connected in a communicable manner as a memory may be used. Furthermore, some processing may be distributed and executed by other processing devices, including other server devices. In other words, the processing device 100 is not limited to a single device, but may be distributed across multiple devices depending on the information handling and processing load.

[0039] The processor 111 functions as a control unit that controls other components of the processing system 1 based on a processing program stored in the memory 112. The processor 111 is mainly composed of one or more CPUs, but may also be combined with a GPU, FPGA, etc. as appropriate. Based on the processing program stored in the memory 112, the processor 111 executes processing to generate first evaluation information based on measurement information and to generate evaluation result information based on second evaluation information by an expert who has been given permission to access the first evaluation information in advance. Specifically, the processor 111 performs the following processes based on a processing program stored in the memory 112: "receiving measurement information obtained by measuring at least a part of the subject's body using a measurement device," "obtaining first evaluation information indicating the results of evaluating the subject's condition by inputting the measurement information into a learned evaluation model for evaluating the subject's condition," "sending the first evaluation information to an expert terminal device usable by one or more first experts who have been given prior permission by the organization to access the first evaluation information," "receiving from the expert terminal device second evaluation information input by one or more first experts indicating the results of evaluating the first evaluation information," and "generating evaluation result information related to the results of evaluating the condition based on the second evaluation information."

[0040] The memory 112 functions as a storage unit and is composed of RAM, ROM, non-volatile memory, HDD, SSD, etc. The memory 112 stores instructions and commands for various control operations of the processing system 1 according to this embodiment as processing programs. Specifically, the memory 112 stores programs for the processor 111 to execute, for example, "receiving measurement information obtained by measuring at least a portion of the subject's body using a measurement device," "acquiring first evaluation information indicating the results of an evaluation of the subject's condition by inputting the measurement information into a trained evaluation model for evaluating the subject's condition," "transmitting the first evaluation information to an expert terminal device available to one or more first experts who have been granted access to the first evaluation information in advance by the organization," "receiving from the expert terminal device second evaluation information indicating the results of an evaluation of the first evaluation information input by one or more first experts," and "generating evaluation result information related to the results of the condition evaluation based on the second evaluation information." In addition to these programs, the memory 112 also stores various information stored in the subject management table, the expert management table, etc. It should be noted that this information does not need to be constantly stored in the memory 112 within the processing device 100, but may be stored in a database device installed remotely. In this case, the database device is also included in the memory 112.

[0041] The communication interface 113 functions as a notification unit for transmitting and receiving various information between the measuring device 200-1, the expert terminal device 200-2, and other processing devices connected via a wired or wireless network. Examples of the communication interface 113 include a wired communication connector such as USB or SCSI, a wireless communication transmitting / receiving device such as a wireless LAN, Bluetooth (registered trademark), or LTE wideband wireless communication, or an infrared wireless communication, and various connection terminals for printed circuit boards or flexible circuit boards.

[0042] FIG. 2B is a block diagram showing the configuration of a terminal device 200 according to an embodiment of the present disclosure. Specifically, FIG. 2B is a block diagram showing the configuration of a device that can be used as the measurement device 200-1 or the expert terminal device 200-2. Note that, in the following, a case will be described in which the measurement device 200-1 is a medical oral cavity imaging device, and therefore a case will be described in which the measurement device 200-1 is provided with a camera 216 that measures the subject's body. However, it goes without saying that various measurement devices (e.g., an electrocardiograph device) other than the camera 216 can be included depending on the type of measurement information of the subject's body.

[0043] The terminal device 200 includes a processor 211, a memory 212, an input interface 213, an output interface 214, a communication interface 215, and a camera 216. These components are electrically connected to one another via control lines and data lines. Note that the terminal device 200 does not need to include all of the components shown in FIG. 2B; it is possible to omit some of the components or add other components. Furthermore, for example, when the terminal device 200 functions as the expert terminal device 200-2, it is not necessary to include the camera 216.

[0044] The processor 211 functions as a control unit that controls the other components of the terminal device 200 based on a program stored in the memory 212. The processor 211 is mainly composed of one or more CPUs, but may also be combined with a GPU, FPGA, or the like as appropriate.

[0045] When functioning as measurement device 200-1, processor 211 executes processes for measuring the subject's body (for example, processes for capturing a subject image having at least a part of the subject's oral cavity as a subject) based on a processing program stored in memory 112. Specifically, processor 211 executes, based on the program stored in memory 212, "processing for accepting a user's operational input via input interface 213 and activating camera 216," "processing for accepting a user's operational input via input interface 213, selecting a subject to be measured from multiple subjects, and capturing a subject image including at least a part of the subject as a subject using camera 216 as measurement information," and "processing for transmitting the captured subject image to processing device 100 via communication interface 215 as measurement information," etc.

[0046] Furthermore, when functioning as the expert terminal device 200-2, the processor 211 executes, based on the processing program stored in the memory 112, a process for evaluating first evaluation information generated based on measurement information and generating second evaluation information, etc. Specifically, based on the program stored in the memory 212, the processor 211 executes, based on the program stored in the memory 212, the following processes: "a process of transmitting, via the communication interface 215, an access permission request for requesting the processing device 100 to access the processing device 100 or information generated by the processing device 100 or stored in the processing device 100," "a process of receiving, from the processing device 100, the first evaluation information generated based on measurement information," "a process of accepting, via the input interface 213, an operation input from a first expert who is permitted to access the first evaluation information, and generating second evaluation information as a result of evaluating the received first evaluation information," and "a process of transmitting the generated second evaluation information to the processing device 100 via the communication interface 215."

[0047] The memory 212 is composed of RAM, ROM, non-volatile memory, HDD, etc., and functions as a storage unit. The memory 212 stores instructions and commands for various controls of the processing system 1 according to this embodiment as programs.

[0048] Specifically, when functioning as measurement device 200-1, memory 212 stores programs to be executed by processor 211, such as "a process of accepting a user's operational input via input interface 213 and activating camera 216," "a process of accepting a user's operational input via input interface 213, selecting a subject to be measured from multiple subjects, and capturing a subject image including at least a portion of the subject as a subject using camera 216 as measurement information," and "a process of transmitting the captured subject image via communication interface 215 to processing device 100 as measurement information."

[0049] Furthermore, when the memory 212 functions as the expert terminal device 200-2, it stores programs to be executed by the processor 211, such as "a process of sending an access permission request via the communication interface 215 to the processing device 100 to request access to the processing device 100 or information generated by the processing device 100 or stored in the processing device 100," "a process of receiving first evaluation information generated based on measurement information from the processing device 100 via the communication interface 215," "a process of accepting operation input from a first expert who is permitted to access the first evaluation information via the input interface 213 and generating second evaluation information which is the result of evaluating the received first evaluation information," and "a process of transmitting the generated second evaluation information to the processing device 100 via the communication interface 215."

[0050] The input interface 213 functions as an input unit that accepts operational inputs from a user or expert to the terminal device 200. Examples of the input interface 213 include physical key buttons and a touch panel having an input coordinate system corresponding to the display coordinate system of the display. In the case of a touch panel, icons are displayed on the display, and the operator selects each icon by inputting operational inputs via the touch panel. The method for detecting the operational inputs of the subject using the touch panel may be any method, such as a capacitive method or a resistive method. The input interface 213-2 does not always need to be physically provided on the terminal device 200, and may be connected as needed via a wired or wireless network. Therefore, in addition to the above, a mouse, a keyboard, etc. can also be used as the input interface 213-2.

[0051] The output interface 214 functions as an output unit for outputting various information. An example of the output interface 214 is a display, but the output interface 214 is not limited to this and may be composed of other liquid crystal panels, organic EL displays, plasma displays, printers, etc. Also, a display does not have to be provided. For example, an interface for connecting to a display or the like connectable to the processing device 100 via a wired or wireless network can function as the output interface 214 for outputting display data to the display or the like.

[0052] The communication interface 215 functions as a communication unit for transmitting and receiving information to and from the processing device 100, other terminal devices 200, and other processing devices. Examples of the communication interface 215 include a connector for wired communication such as USB or SCSI, a transmitting and receiving device for wireless communication such as broadband wireless communication such as wireless LAN, Bluetooth (registered trademark), or LTE, or infrared, and various connection terminals for printed circuit boards and flexible circuit boards.

[0053] The camera 216 is an example of a measurement device for measuring the subject's body measurement information and functions as an imaging unit that detects light reflected from the subject and generates an image of the subject. To detect the light, the camera 216 includes, for example, a CMOS image sensor, a lens system, and a drive system for achieving the desired functions. The image sensor is not limited to a CMOS image sensor; other sensors, such as a CCD image sensor, can also be used. Although not specifically shown, the camera 216 may have an autofocus function, and is preferably set, for example, so that the focus is set on a specific part of the subject's body at the front of the lens. Furthermore, the camera 216 may have a zoom function and is preferably set to capture an image at an appropriate magnification depending on the size of the subject.

[0054] The camera 216 is included in the measurement device 200-1 when an image of a subject is used as measurement information. However, it is possible to include various measurement devices (e.g., an electrocardiograph device) other than the camera 216 depending on the type of measurement information of the subject's body. Furthermore, when the terminal device 200 functions as the expert terminal device 200-2, it is not necessarily required to include a measurement device including the camera 216.

[0055] 3. Various information used in processing in the processing system 1 3A and 3C show various tables that store information that is stored in the processing device 100 and that is sent to and received from each terminal device 200 as the processing progresses. This information is updated and stored as needed as the processing progresses. Note that the information shown in FIGS. 3A and 3B may be stored in the memory 112 of the processing device 100, or may be stored in another database device installed remotely and read out as needed as the processing progresses.

[0056] 3A is a diagram conceptually illustrating a subject management table stored in the processing device 100 according to an embodiment of the present disclosure. According to FIG. 3A, the subject management table stores measurement information, evaluation information, first evaluation information, second evaluation information, expert (evaluation) information, expert (diagnosis, etc.) information, and other attribute information in association with subject ID information. Each of these pieces of information is used as attribute information indicating various attributes of the subject.

[0057] "Subject ID information" is information unique to each subject for identifying each subject. As an example, subject ID information is generated each time a new subject is registered by a user. However, the subject ID information may be any information that can identify the subject as described above, and various information such as the subject's name or username may be used. Note that, unlike the first expert, the subject identified by the subject ID information does not necessarily need to be granted in advance by the organization access to the processing device 100 or to information generated by the processing device 100 or information stored in the processing device 100 (e.g., first evaluation information).

[0058] "Measurement information" refers to information obtained by measuring the subject's body using measurement device 200-1 or the like. The measurement information preferably includes information obtained by medically measuring the subject's body, and more preferably includes various information such as subject images of at least a portion of the subject, CT images, MRI images, PET images, electrocardiogram data, ultrasound data, X-ray images, sound data of the lungs, heart, or intestines, sound data of the subject's speech, coughing, etc., behavioral pattern data of the subject, and walking data of the subject. Such measurement information may be the various types of information exemplified above itself, or may be information obtained by performing various processes such as sharpening, screening, segmentation, and feature extraction on the various types of information exemplified above.

[0059] In particular, when a subject image is used as measurement information, image data including at least the subject's oral cavity as a subject measured (specifically, photographed) using the measurement device 200-1 or the like is preferably used. The image data may be one or more still images, one or more videos, or a combination thereof. The subject image is stored by being received from the measurement device 200-1 via the communication interface 113. The subject image may be the image data itself measured (specifically, photographed) by the measurement device 200-1, or may be data obtained by performing image processing such as sharpening, screening, and segmentation on the image data. The subject image is typically useful for evaluating the condition of the oral cavity and the prevalence of diseases associated with oral findings. Note that such subject image may contain various information analyzed from the image data instead of or in addition to the image data. For example, the image data may include values, classifications, and categories obtained by inputting the image data into a trained analysis model and quantifying various image features (e.g., data indicating the degree of lip moistness).

[0060] Here, FIG. 3B is a diagram conceptually illustrating an example of a subject image according to an embodiment of the present disclosure. Specifically, FIG. 3B is a diagram illustrating an example of the configuration of subject image C1 measured (specifically, photographed) as an example of measurement information for a subject whose subject ID information is "A1." According to FIG. 3B, subject image C1 includes one or more images (in FIG. 3B, multiple subject images C1-1 to C1-n). Such subject image C1 can be composed of frames captured in a video, or can be composed of multiple still images captured multiple times.

[0061] As shown in images (a) to (d), such subject image C1 is an image that includes at least the subject's pharynx as at least a part of the subject's oral cavity. Note that subject image C1 may be, for example, an image of the subject's lips, an image of the subject's tongue, an image of the inside of the subject's oral cavity, or an image of the subject's teeth and gums. Furthermore, the subject image C1 is not limited to these, and any image that includes at least a part of the subject can be used, such as an image of another natural orifice of the subject, an image of the subject's body surface or interior, or an image of the subject's sublingual area. Note that while subject image C1 is an image composed of multiple images, the subject image may also be a single image. Note that the subject's body, which is the subject of measurement information, may include not only the subject's body itself (for example, at least a part of the oral cavity) but also an appliance, such as an appliance, worn on the subject's body.

[0062] Returning to Fig. 3A again, "evaluation information" is information that can be used to evaluate the subject's physical condition. The evaluation information is acquired by being input via the measurement device 200-1, the expert terminal device 200-2, or other terminal device based on a request from the processing device 100 during the process related to the evaluation of the subject's physical condition, or by being input by the measurement device 200-1 together with the measurement information. The evaluation information may also be acquired during a series of processes from a database device that stores interview information, findings information, medical record information, etc. Examples of such evaluation information include patient background such as allergies and underlying diseases, body temperature, peak body temperature since onset, time elapsed since onset, heart rate, pulse rate, oxygen saturation, blood pressure, medication status, contact with other infectious disease patients, presence or absence of subjective symptoms and physical findings such as joint pain, muscle pain, headache, fatigue, loss of appetite, chills, sweating, cough, sore throat, runny nose / nasal congestion, tonsillitis, gastrointestinal symptoms, rash on the hands and feet, redness or white coating of the pharynx, swollen tonsils, history of tonsillectomy, strawberry tongue, swollen anterior cervical lymph nodes with tenderness, history of infectious disease vaccination, timing of vaccination, presence or absence of decreased or changed vision, pain or pressure in the eyes, changes in visual field Examples of information for evaluation include information on medical history such as the presence or absence of blackouts, the presence or absence of chest pain or chest discomfort, its nature, frequency, aggravating factors and remission factors, the presence or absence of shortness of breath or palpitations, its nature, frequency, aggravating factors and remission factors, past history of heart disease, daily activity level, stress and anxiety, lifestyle habits (exercise, diet, smoking, drinking), family history (presence or absence of heart disease), recent weight fluctuations, the time period and frequency of symptoms such as arrhythmia, the location and timing of abnormal sounds during auscultation, fatigue, edema, dizziness, the presence or absence of cough, the time period and nature of cough (dry, wet), the presence or absence of dyspnea, body temperature, the presence or absence of contact with nearby infectious disease patients, the color and amount of sputum, and developmental history.In addition to the above, examples of evaluation information include information on findings indicating abnormalities obtained by various examinations of the subject such as visual examination, interview, palpation, auscultation, or percussion, as well as tests to assist in the assessment.

[0063] Each of these pieces of evaluation information can be obtained by, for example, having a user or an expert interview the subject, having a user or an expert give a comment to the subject, obtaining output information by image analysis of a subject image that can be used as measurement information (for example, inputting it into a trained analysis model), or using other devices (for example, a thermometer, a blood pressure monitor, or a pulse oximeter). However, any method of obtaining the evaluation information is acceptable.

[0064] "First evaluation information" is information indicating the results of an evaluation of the subject's condition. The first evaluation information is information that can only be accessed by a person (first expert) who has been granted access in advance by the organization. Examples of such first evaluation information include information obtained by inputting measurement information into a trained evaluation model, information obtained by inputting evaluation information into a trained evaluation model, information obtained by performing arbitrary data analysis processing on the measurement information, information obtained by accepting evaluation results from an arbitrary expert, etc., on the measurement information, and combinations thereof. Note that the following describes a case where information obtained by inputting a subject image, which is one piece of measurement information, into a trained evaluation model is used as the first evaluation information, but of course other information, including the information exemplified above, can also be used.

[0065] The "second evaluation information" is information indicating the results of evaluating the first evaluation information. The second evaluation information is accessible not only to first experts who have been granted access to the first evaluation information by the organization, but also to other parties. Examples of such second evaluation information include information evaluated by a first expert who has been granted access by the organization, information obtained by inputting measurement information and / or the first evaluation information into a trained evaluation model, information obtained by inputting evaluation information and / or the first evaluation information into a trained evaluation model, information obtained by performing arbitrary data analysis processing on the measurement information, and combinations thereof. Note that the following describes a case in which the second evaluation information is information evaluated by a first expert selected from among multiple experts who have been granted access to the first evaluation information by the organization. However, it is also possible to use other information, including the information exemplified above. The second evaluation information may take any form, such as information indicating that the first evaluation information has simply been "checked," information indicating corrections, or all information after corrections have been made to the first evaluation information. As described above, the correction of the first evaluation information may include various corrections such as replacing the content of the first evaluation information, adding content to the first evaluation information, deleting content from the first evaluation information, etc. In other words, the second evaluation information may be the first evaluation information to which, for example, another evaluation result obtained by a first expert has been added.

[0066] In the following description, the first evaluation information is generated using a trained evaluation model, and the second evaluation information is generated using an expert. However, the present invention is not limited to this, and the evaluation information may be generated by any combination of evaluation methods and evaluation entities, as described below. The first evaluation information is generated using the first expert, and the second evaluation information is generated using the trained evaluation model. The first evaluation information is generated using a trained evaluation model, and the second evaluation information is generated using another trained evaluation model. Both the first and second evaluation information are generated using the same evaluation method.

[0067] The "expert (evaluation) information" is information indicating the first expert who input the second evaluation information. Typically, expert ID information for identifying the expert who input the second evaluation information is stored. For example, such an expert is preferably an expert who has been granted access to the processing device 100 by the organization in advance, or to information generated by the processing device 100 or information stored in the processing device 100 (e.g., the first evaluation information). However, the first expert may be selected from among multiple experts who have been granted access by the organization in advance, as described above, based on at least one of the subject's attribute information and the first evaluation information, and the attribute information of each expert. In other words, the subject and the expert may be matched. This makes it possible to select a more appropriate expert as the first expert.

[0068] The "expert (diagnosis, etc.) information" is information indicating a second expert who is a specialist who can provide services such as diagnosis and care to the subject to improve or maintain the subject's physical condition. Unlike the first expert, such an expert does not necessarily need to be authorized by the organization in advance to access the processing device 100 or to access information generated by the processing device 100 or information stored in the processing device 100 (e.g., the first evaluation information). Typically, expert ID information for identifying an expert who can provide the above-mentioned service is stored. Examples of such experts are as exemplified above. However, it is desirable that one or more second experts be selected from among these multiple experts based on at least one of the subject's attribute information and the second evaluation information, and the attribute information of each expert, i.e., the subject and the experts are matched. This makes it possible to select a more appropriate expert as the second expert.

[0069] "Other attribute information" is information indicating attributes of each subject other than the above-mentioned information. Typical examples of such information include information identifying the organization to which the subject belongs (organization-specific information), the subject's authentication information, location information indicating the place of residence, age, weight, sex, and information on family members living together. The other attribute information is input, for example, via an input interface of the measurement device 200-1 or the user terminal device, and is stored by being received from the measurement device 200-1 or the user terminal device via the communication interface 113.

[0070] FIG. 3C is a diagram conceptually illustrating an expert management table stored in the processing device 100 according to an embodiment of the present disclosure. According to FIG. 3C, the expert management table stores permission information, expertise information, operation information, location information, expert evaluation information, and other attribute information in association with expert ID information. Each piece of information is used as attribute information indicating various attributes of the experts. Note that each piece of information, such as the expertise information, may be input in advance by the expert terminal device 200-2 or another terminal device. Furthermore, each piece of information, such as the expert information, may be acquired during the exemplary process from, for example, a management device managed by the organization to which each expert belongs.

[0071] "Expert ID information" is information unique to each expert that identifies each expert. For example, the expert ID information is generated each time a new subject is registered by the expert. However, the expert ID information can be anything that can identify the expert as described above, and various information such as the expert's name or user name can be used.

[0072] The "permission information" is information indicating whether or not an organization has permitted access to the processing device 100, or to information generated by the processing device 100 or information stored in the processing device 100 (for example, the first evaluation information). As an example, the information stores information indicating either "permission" or "restriction (indicating that access is not permitted)." That is, the information indicating "permission" is stored when the expert terminal device 200-2 transmits an access permission request for the above access to the processing device 100 in advance, such as requesting registration for use of a service provided by the processing system 1, and the access is permitted by the processor 111 of the processing device 100.

[0073] "Expertise information" is information indicating the area of ​​expertise of each expert. Examples of such expertise information include information indicating the areas that each expert can evaluate and the level of physical condition, information indicating the specialty of a doctor or dentist, such as internal medicine, pediatrics, otolaryngology, pediatric dentistry, orthodontics, ophthalmology, cardiology, pulmonology, oral surgery, or dentistry, qualification information such as the qualifications, title, or license held by each expert, information indicating the occupation or position of each expert, information indicating the years of experience of each expert, information indicating the career history of each expert, and combinations thereof. The expert information is preferably used when matching a subject with a first expert or a second expert.

[0074] "Operation information" is information indicating the working hours of each expert. Examples of such operation information include schedule information for each expert, attendance information for each expert, scheduled work information for each expert, and combinations thereof. In other words, operation information may be any information indicating the working hours of each expert, such as information on the hours, dates, days of the week, months, and years when the expert is available to work, or information on the hours, dates, days of the week, months, and years when the expert is unavailable to work. Furthermore, operation information is not limited to the above, and log information such as whether each expert has logged in to a service provided by the processing system 1 and the time when the expert accessed the service may also be used. Operation information is preferably used when matching a target person with a first expert or a second expert.

[0075] "Location information" is information indicating the location where each expert is located. Examples of such location information include coordinates such as latitude and longitude, addresses, postal codes, names of facilities or buildings, names of affiliations, location information detected based on the expert terminal device 200-2, and combinations thereof. The location information is preferably used when matching a subject with a first expert or a second expert.

[0076] "Expert evaluation information" is information showing the evaluation results for each expert. Such expert evaluation information is not limited to the information exemplified below, but examples include the following information. For example, by using "tendency information such as the age group and gender of subjects previously evaluated by each expert" as expert evaluation information, it becomes possible to preferentially match subjects of an age group or gender in which the subject has extensive experience or is skilled. The expert evaluation information is preferably used when matching a subject with a first expert or a second expert. Information showing the accuracy of the second evaluation information previously generated by each expert Information based on the time required to transmit the past second evaluation information to the processing device 100 Information indicating the difficulty of the evaluation of the first evaluation information Information showing the types and number of illnesses each expert has evaluated in the past Information showing the number of first evaluations each expert has evaluated in the past - Trend information such as age group and gender of subjects evaluated by each expert in the past · Reviews from users and target audiences Qualifications held by each expert Title or license or other qualification information Information indicating the occupation or position of each expert Information indicating the years of experience of each expert, and information indicating the career history of each expert -Paper information submitted by each expert Combination of the information listed above

[0077] The information indicating the accuracy of the second evaluation information previously generated by each expert, the information indicating the number of first evaluation information previously evaluated by each expert, trend information such as the age group and gender of the subjects previously evaluated by each expert, and word-of-mouth information from each user or subject may be reset when a certain period of time has passed since the information was input. For example, the information indicating the number of first evaluation information previously evaluated is decremented by one six months after the first evaluation information was evaluated. The expert evaluation information may also be reset when the number of evaluations of the first evaluation information within a given period of time is less than a predetermined number. For example, if the number of evaluations of the first evaluation information within the past six months is less than one (i.e., zero), the expert evaluation information is reset. This allows the expert evaluation information to always reflect the latest situation.

[0078] "Other attribute information" is information indicating attributes of each expert other than the above-mentioned information. Typical examples of such information include information identifying the organization to which the expert belongs (organization identification information), contact information, operating hours such as consultation hours, availability of home visits, map information, and combinations thereof.

[0079] 4. Processing sequence executed by processing system 1 Fig. 4 is a diagram showing a processing sequence executed in the processing system 1 according to an embodiment of the present disclosure. Specifically, Fig. 4 shows a processing sequence executed among the processing device 100, the measurement device 200-1, and the expert terminal device 200-2. Of these, S01 to S06 show an access permission request process by the expert to the processing device 100, etc., and S11 to S22 show a process for generating evaluation result information on the subject's condition. Each process shown in this processing sequence is mainly executed by a processor in each device processing a program stored in memory.

[0080] (A) Expert Access Permission Request Processing First, the process of an expert requesting access permission to the processing device 100, etc. will be described. As shown in Fig. 4, the processor 211 of the expert terminal device 200-2 accepts an operation input from the expert via the input interface 213, and starts an application program for using the service provided by the processing system 1 (S01). The processor 211 of the expert terminal device 200-2 accepts an operation input from the expert via the input interface 213, and selects whether to register the expert as an expert in the service and as a first expert who evaluates the first evaluation information, or to register only a second expert (S02). Here, the case of registration as a first expert will be described.

[0081] When registration as a first expert is selected, the processor 211 of the expert terminal device 200-2 accepts operational input of the expert via the input interface 213 and inputs various attribute information such as expertise information, operation information, location information, or other attribute information to be stored in the expert management table (S03). When this attribute information is input, the processor 211 of the expert terminal device 200-2 transmits an access permission request (T01) to the processing device 100 via the communication interface 215 to request permission to access the processing device 100, or to access information generated by the processing device 100 or information stored in the processing device 100.

[0082] When the processor 111 of the processing device 100 receives an access permission request via the communication interface 113, it generates new expert ID information and stores the received attribute information in association with the expert ID information. Next, the processor 111 of the processing device 100 determines whether to grant access to the expert who sent the access permission request based on the judgment of the organization that manages the service and the input attribute information, etc. (S04). Then, if the processor 111 of the processing device 100 determines that access is permitted, it updates the permission information in the expert management table to "permitted" and stores the updated information (S05).

[0083] The processor 111 of the processing device 100 transmits permission information (T02), which is the result of the judgment on the access permission request, to the expert terminal device 200-2 that has transmitted the access permission request, via the communication interface 113. When the processor 211 of the expert terminal device 200-2 receives the permission information via the communication interface 215, it outputs the received permission information via the output interface 214 (S06). This ends the processing flow.

[0084] In this way, each expert requests permission in advance to access the processing device 100 or to access information generated by or stored in the processing device 100. As a result, the expert is registered as an expert who can access information with restricted access, such as the first evaluation information. Conversely, the processing device 100 can use permission information to restrict access to the processing device 100 or information generated by or stored in the processing device 100 (e.g., the first evaluation information). In this way, by limiting access to the processing device 100, etc., to a limited number of people who are authorized in advance, the distribution of information generated by or stored in the processing device 100 is restricted, enabling more flexible operation of the processing device 100.

[0085] (B) Processing for generating evaluation result information of the subject's condition Next, a process for generating evaluation result information of the subject's condition will be described. In the following, a case will be described in which "subject images captured with at least a part of the subject's oral cavity as the subject" are used as measurement information to evaluate the subject's condition as "whether the subject has a disease (e.g., influenza) in which findings are found in the oral cavity of the subject." However, it goes without saying that similar processing can be performed even when other measurement information is used to evaluate other conditions.

[0086] According to FIG. 4, the processor 211 of the measuring device 200-1 accepts a user's operational input via the input interface 213 and activates the measuring device 200-1 (S11). Then, the processor 211 accepts the user's operational input via the input interface 213 and selects a subject to be evaluated. The processor 211 also accepts the user's operational input via the input interface 213 and activates the camera 216, which is an example of a measurement device that measures measurement information. The processor 211 uses the activated camera 216 to capture a subject image including at least a portion of the subject's oral cavity as the subject (S12). After capturing the subject image, the processor 211 transmits the captured subject image, subject ID information of the subject, and a request for evaluation of the subject's physical condition (T11) to the processing device 100 via the communication interface 215. Although not specifically shown in FIG. 4, various image processing such as sharpening and screening may be performed on the captured subject image.

[0087] When the processor 111 of the processing device 100 receives the evaluation request via the communication interface 113, it stores the received subject image in the measurement information of the subject management table in association with the received subject ID information (S13). In addition, the processor 111 receives evaluation information and other attribute information from the measurement device 200-1 or other devices (for example, the expert terminal device 200-2 or a database device) via the communication interface 113 as needed, and stores them in association with the subject ID information (S14).

[0088] Based on the received subject ID information, the processor 111 of the processing device 100 reads out the subject image stored in the measurement information of the subject management table, and, if necessary, the evaluation information and other attribute information, and executes evaluation processing using the subject image, etc. (S15). One example of the evaluation processing is performed by providing the subject image, and, if necessary, the evaluation information and other attribute information as input information to a trained evaluation model, and obtaining information indicating the evaluation result of the subject's physical condition as output information. Details of this processing will be described with reference to FIG. 5A, etc.

[0089] Next, the processor 111 of the processing device 100 generates first evaluation information based on the output information acquired by the evaluation process (S16), and stores the generated first evaluation information in association with the subject ID information in the subject management table. As an example of this process, based on the output information acquired from the trained evaluation model, the first evaluation information is generated in a report format including the input subject image, evaluation information, other attribute information, and values, classifications, or categories indicating physical conditions output from the trained evaluation model. Details of this process will be described with reference to FIG. 5A etc.

[0090] Next, the processor 111 of the processing device 100 executes a matching process to select an expert (first expert) who will evaluate the first evaluation information (S17). As an example of this process, the processor 111 references permission information from the expert management table and extracts multiple experts who are permitted to access the first evaluation information (experts whose permission information is stored with "permission" associated with it). Then, the processor 111 selects one or more experts from the multiple experts who are permitted to access the first evaluation information in advance, based on at least one of the attribute information of the subject and the first evaluation information, and the attribute information of each of the extracted multiple experts. At this time, the processor 111 calculates a priority score indicating the matching priority for each expert, and selects one or more experts with the highest score. Details of this process will be described with reference to FIG. 5A, etc.

[0091] Next, when an expert (first expert) to evaluate the first evaluation information is selected, the processor 111 of the processing device 100 transmits an evaluation request (T12) including the first evaluation information, subject ID information, and a request for evaluation of the first evaluation information to the expert terminal device 200-2 of the selected expert (first expert) via the communication interface 113.

[0092] When the processor 211 of the expert terminal device 200-2 receives an evaluation request via the communication interface 215, it evaluates the received first evaluation information and generates second evaluation information. As an example of this processing, the processor 111 outputs the received first evaluation information via the output interface 214. Then, the processor 111 accepts an operation input from the expert who references the first evaluation information output via the input interface 213, and inputs a confirmation result as to whether the first evaluation information is acceptable as is, or inputs a correction to the first evaluation information. In this way, the processor 111 generates second evaluation information.

[0093] 7A is a diagram showing an example of a first evaluation information screen output on the expert terminal device 200-2 according to an embodiment of the present disclosure. Specifically, FIG. 7A is a diagram showing an example of a first evaluation information screen 10 output via the output interface 214 in S12 of FIG. 4. According to FIG. 7A, the first evaluation information screen 10 includes at least an evaluation result display area 11, measurement information display areas 12a to 12d, a medical interview information display area 13, and a finding information display area 14.

[0094] The evaluation result display area 11 includes information such as "Possibility of influenza: Yes," which is the result of evaluating the subject's condition, which is output information obtained from the trained evaluation model. Note that while the information "Yes" is displayed in FIG. 7A, "Suspected" or "No" may also be displayed depending on the output information. Also, while FIG. 7A displays categories, the categories may be displayed together with numerical values ​​indicating the possibility, or only numerical values ​​may be displayed, or more detailed categories or numerical values ​​may be displayed in addition to the three categories. Also, although not shown in FIG. 7A, information indicating the accuracy of the evaluation output from the trained evaluation model may be displayed.

[0095] The measurement information display areas 12a to 12d are areas for displaying subject images and the like acquired as measurement information. Here, for the sake of convenience of explanation, four subject images (still images) input as input information to the trained evaluation model are displayed, but naturally, four or more subject images (still images) or videos may be displayed. Furthermore, it is not necessary to display all subject images acquired as measurement information, and one or more selected subject images may be displayed. The one or more subject images may be selected by matching with a predetermined reference image (reference image), or may be selected using a trained selection model. Furthermore, the medical interview information display area 13 and the finding information display area 14 display medical interview information and finding information stored as evaluation information.

[0096] Here, the evaluation result information that is finally output is expected to be referenced by users or subjects, whereas the first evaluation information is information that is referenced by experts. Therefore, there is a high possibility that the first evaluation information has more specialized knowledge. The first evaluation information output on the first evaluation information screen 10 may include content different from the evaluation result information. Specifically, the output information obtained from the trained evaluation model displayed in the evaluation result display area 11 may be output as is as more precise numerical values ​​or scores indicating the subject's condition without any particular classification or division. Furthermore, information indicating the accuracy of the judgment obtained from the trained evaluation model may be output in the evaluation result display area 11.

[0097] In this way, by displaying information different from the evaluation result information as the first evaluation information to be referred to by the expert, a more accurate evaluation can be achieved.

[0098] For ease of explanation, FIG. 7A illustrates four measurement information display areas 12a to 12d as an example of the measurement information display area. However, the measurement information display area may include fewer than four or more than five areas. In the example of FIG. 7A, only an image of the pharynx is displayed as the subject image because the possibility of influenza is being evaluated. However, in cases where a comprehensive evaluation of the oral cavity is performed as part of the subject's condition, such as an OHAT, different images may be displayed for each area, such as an image of the lips, tongue, oral cavity interior, teeth, and gums. The measurement information display area may also include not only these image information but also information indicating at least one of the conditions indicated on the OHAT evaluation sheet, i.e., lips, sublingual, gums, or oral mucosa, saliva, remaining teeth, dentures, oral hygiene, and toothache, at least one of the conditions of swallowing function and masticatory function, and at least one of the conditions of tongue movement, lip movement, oral moisture level, and mouth opening. Information on swallowing function, chewing function, tongue movement, lip movement, degree of oral moisture, and degree of mouth opening is expected to have an impact on and be correlated with the OHAT assessment. Therefore, displaying this information together with information on the OHAT will enable experts to make a more appropriate assessment.

[0099] Furthermore, for example, when a person is infected with influenza, characteristic findings called follicles may be observed near the pharynx. Furthermore, in addition to the pharynx, various parts of the oral cavity are present in the subject image. Therefore, although not specifically shown in FIG. 7A , the subject image may be processed by the processor 111 to detect parts such as follicles and the oral cavity, and segmentation information for each part, such as a bounding box, may be superimposed and displayed on the subject image. This allows the first expert to accurately grasp information about each part and appropriately evaluate the first evaluation information.

[0100] Furthermore, for example, techniques such as Grad-CAM can be used to display the basis and reasons for the evaluation of the trained evaluation model and the areas of interest in the input object image. Therefore, the processor 111 inputs input information such as the object image into an evaluation basis visualization model such as Grad-CAM and obtains heat map information indicating the areas of interest in the object image as output. The processor 111 then displays the heat map information superimposed on each object image in the measurement information display areas 12a-12d or separately from these object images. The processor 111 also inputs the heat map information output from the evaluation basis visualization model, the object image, and the evaluation results of the trained evaluation model into a large-scale language model and provides a prompt to the large-scale language model to instruct it to output the basis of the evaluation results, thereby obtaining the basis and reasons for the evaluation by the trained evaluation model in natural language format. The processor 111 then outputs the basis and reasons for the evaluation expressed in natural language format together with the evaluation results. This allows the first expert to accurately grasp the basis of the evaluation results and appropriately evaluate the first evaluation information.

[0101] The segmentation information, partition information, and heat map information exemplified above may be turned on and off as appropriate by receiving an operation input from the first expert, allowing the first expert to freely select between a plain subject image and a subject image with various supplemental information added.

[0102] Next, Fig. 7B is a diagram showing an example of a second evaluation information screen output on the expert terminal device 200-2 according to an embodiment of the present disclosure. Specifically, Fig. 7B is a diagram showing an example of the second evaluation information screen 20 output via the output interface 214 in S12 of Fig. 4. That is, the second evaluation information screen 20 is a screen displaying the second evaluation information after the first evaluation information has been corrected by the expert. According to Fig. 7B, the second evaluation information screen 20 includes at least an evaluation result display area 21, measurement information display areas 22a to 22d, an interview information display area 23, and a finding information display area 24.

[0103] The evaluation result display area 21 includes information after correction of the information displayed in the evaluation result display area 11 of the first evaluation information screen 10. In the example of FIG. 7B, as a result of the evaluation by the expert who referred to the first evaluation information, it is shown that the possibility of influenza infection has been corrected from "yes" to "suspected" by receiving an operational input from the expert via the input interface 213. Note that, although the explanation here is based on the assumption that the possibility of influenza infection has been corrected, there are of course cases where the oral evaluation score on the first evaluation information screen 10 is displayed as is without any correction.

[0104] Moreover, the measurement information display areas 22a to 22d are areas for displaying subject images and the like acquired as measurement information. Specifically, the measurement information display areas 22a to 22d display four subject images input as input information to the trained evaluation model. As described in FIG. 7A, any of the subject images may be displayed here. The subject images displayed in the measurement information display areas 22a to 22d are replaced with a more suitable image from among other images captured as subject images, if any, as a result of evaluation by an expert who references the first evaluation information, by receiving operational input from the expert via the input interface 213. Furthermore, the medical interview information display area 23 and the finding information display area 24 display medical interview information and finding information stored as evaluation information, but these pieces of information can also be modified by receiving operational input from the expert via the input interface 213.

[0105] For ease of explanation, FIG. 7B illustrates four measurement information display areas 22a to 22d as an example of the measurement information display area. However, the measurement information display area may include fewer than four or more than five areas corresponding to the first evaluation information screen 10. In the example of FIG. 7B, only an image of the pharynx is displayed as the subject image because the possibility of influenza is being evaluated. However, in cases where a comprehensive evaluation of the oral cavity is performed as part of the subject's condition, such as an OHAT, different images may be displayed for each area, such as an image of the lips, tongue, oral cavity interior, teeth, and gums. The measurement information display area may also include not only these image information but also information indicating at least one of the conditions indicated on the OHAT evaluation sheet, i.e., the lips, sublingual area, gums or oral mucosa, saliva, remaining teeth, dentures, oral hygiene, and toothache, at least one of the conditions of swallowing function and masticatory function, and at least one of the conditions of tongue movement, lip movement, oral moisture level, and mouth opening level. Information on swallowing function, chewing function, tongue movement, lip movement, degree of oral moisture, and degree of mouth opening is expected to have an impact on and correlate with the OHAT assessment. Therefore, by displaying this information together with information on the OHAT, professionals providing diagnosis and care and users can obtain more appropriate information.

[0106] 7A and 7B, the second evaluation information screen 20 may be provided with a first expert comment input area in which the rationale for checking or correcting the first evaluation information, notes, etc. can be input in free text format. The information input in the first expert comment input area is stored as part of the second evaluation information, and can be referred to as reference information when the second expert makes a diagnosis, for example.

[0107] Returning to Figure 4 again, once the second evaluation information is generated as shown in Figures 7A and 7B, the processor 211 of the expert terminal device 200-2 transmits the generated second evaluation information and subject ID information to the processing device 100 via the communication interface 215.

[0108] When the processor 111 of the processing device 100 receives the second evaluation information, etc. via the communication interface 113, it stores the received second evaluation information in the subject management table in association with the subject ID information received together (S19). When the processor 111 receives the second evaluation information, it stores the expert ID information of the expert who transmitted the evaluation information in association with the subject ID information in the expert (evaluation) information. Furthermore, the processor 111 executes an evaluation process for the expert who evaluated the first evaluation information based on the received second evaluation information (S20). As an example of this process, the processor 111 performs a process for updating the expert evaluation information associated with the expert. Specifically, for example, upon receiving the second evaluation information, the processor 111 updates information indicating the number of first evaluation information pieces previously evaluated by the expert by adding points for one evaluation. Furthermore, the processor 111 updates information based on the time required for transmitting the second evaluation information to the processing device 100 by adding points depending on the speed of transmission of the second evaluation information after transmitting the evaluation request for the first evaluation information. Conversely, for experts who have not transmitted the second evaluation information within a predetermined time, the score is updated so that points are deducted from the information based on the time it took to transmit the second evaluation information to the processing device 100.

[0109] Next, the processor 111 of the processing device 100 reads at least one of the expertise information, operation information, location information, expert evaluation information, and other attribute information of each expert, and executes a matching process to select an expert who can provide services such as diagnosis and care to the subject (S21). As an example of this process, the processor 111 selects one or more experts from among multiple experts based on the expert's attribute information and the second evaluation information. At this time, the processor 111 calculates a priority score indicating the matching priority for each expert, and selects one or more experts with the highest score. The processor 111 then stores the expert ID information of the selected one or more experts in the expert (diagnosis, etc.) information in association with the subject ID information. Details of this process will be described with reference to FIG. 5B and other figures. It is desirable that the expert (second expert) selected here be selected from among the experts (first expert) who evaluated the first evaluation information. This ensures the objectivity of the evaluation of the first evaluation information.

[0110] Next, when the processor 111 selects an expert (second expert), it reads out various attribute information associated with the second evaluation information, the expert (diagnosis, etc.) information, and the expert ID information, and generates evaluation result information. Then, the processor 111 transmits the generated evaluation result information via the communication interface 113 to the measuring device 200-1 that transmitted the evaluation request (T14).

[0111] When the processor 211 of the measuring device 200-1 receives the evaluation result information via the communication interface 215, it outputs the received evaluation result information via the output interface 214 (S22). The evaluation result information is transmitted to the measuring device 200-1, but it may also be sent to a user terminal device that can be used by the user or the subject, and output from the user terminal device.

[0112] Here, the evaluation result information generated by the processor 111 of the processing device 100 can have different content depending on whether the first expert is at least one of a doctor and a dentist or a specialist other than a doctor or a dentist. That is, there are differences in the actions that doctors and dentists and other specialists are permitted to provide to the subject under legal provisions. Therefore, if the first expert is at least one of a doctor and a dentist, the information can include necessary medical advice tailored to the subject's individual physical and mental condition. Furthermore, if the first expert is a specialist other than a doctor or a dentist, the information can include a recommendation to visit a medical institution to which at least one of the doctor and the dentist belongs.

[0113] 7C is a diagram showing an example of an evaluation result information screen output by the measurement device 200-1 according to an embodiment of the present disclosure. Specifically, FIG. 7C is a diagram showing an example of an evaluation result information screen 30a when second evaluation information is generated by at least one of a doctor and a dentist, among the evaluation result information screens output via the output interface 214 in S22 of FIG. 4. According to FIG. 7C, the evaluation result information screen 30a includes at least an evaluation result display area 31, measurement information display areas 32a to 32d, a medical interview information display area 33, a finding information display area 34, an expert information display area 35, and a medical advice area 37.

[0114] The evaluation result display area 31, like the evaluation result display area 21 on the second evaluation information screen 20, contains information that has been corrected from the information displayed in the evaluation result display area 11 on the first evaluation information screen 10. Note that, although the explanation here is based on the assumption that the possibility of contracting influenza has been corrected, there are of course cases where the possibility of contracting influenza on the first evaluation information screen 10 is displayed as is without any correction.

[0115] Similarly to the measurement information display areas 22a to 22d of the second evaluation information screen 20, the measurement information display areas 32a to 32d are areas for displaying subject images and the like acquired as measurement information. Specifically, the measurement information display areas 32a to 32d display subject images input as input information to the trained evaluation model and other subject images replaced by the first expert. The medical interview information display area 33 and the finding information display area 34 display medical interview information and finding information stored as evaluation information, as well as medical interview information and finding information corrected by the first expert.

[0116] The expert information display area 35 also includes various attribute information of the expert (second expert) selected in S21 of Fig. 4. Specifically, the expert information display area 35 displays, together with the item name "We recommend you visit the following internist," various attribute information such as the name of the affiliation of the doctor or dentist who is the expert, the expert's name, address, whether or not home visits are available, working hours such as consultation hours, expert evaluation information, and map information 36. Note that the attribute information listed here is merely an example, and naturally other attribute information may be displayed, and the information of not only one expert but also multiple experts may be displayed.

[0117] The medical advice area 37 is displayed when the first expert is at least one of a doctor and a dentist. This area includes medical advice that at least one of a doctor and a dentist can provide to the subject (e.g., "There is a high possibility of influenza. Take an antipyretic and monitor your condition for 24 hours before undergoing a re-examination." in the example of FIG. 7C). That is, the medical advice area 37 includes information useful for a doctor or dentist to help maintain or improve the subject's condition. For example, when generating the second evaluation information, the processor 211 of the expert terminal device 200-2 receives an operation input from the first expert via the input interface 213, and generates information indicating such medical advice by the first expert inputting a free-form sentence or inputting a fixed sentence by selecting an option. Alternatively, the processor 111 of the processing device 100 references a pre-stored medical advice table and selects an appropriate fixed sentence from the table based on the generated second evaluation information and evaluation information. Then, the processor 111 of the processing device 100, when referring to the expert (evaluation) information and finding that the first expert is at least one of a doctor and a dentist, provides a medical advice area 37 on the evaluation result information screen 30a and processes the generated information to be included in the medical advice area 37.

[0118] 7C, the case where the medical advice area 37 is included is described, but it is not necessary to include this area if the first expert is a doctor or dentist. Also, in addition to this area, a recommendation to see a doctor may be included.

[0119] Furthermore, while the first evaluation information is intended for reference by experts, the evaluation result information that is ultimately output is expected to be referenced by users or subjects. In other words, it is quite possible that the experts may have less specialized knowledge than the users or subjects. Therefore, the evaluation result information output on the evaluation result information screen 30a may include content different from that of the first evaluation information. Specifically, the precise numerical values ​​or scores indicating the subject's condition in the first evaluation information may be converted into classifications or categories (e.g., "good," "slightly poor," or "pathological") that are easier for users or subjects to understand. Furthermore, information indicating the definitions and meanings of each piece of information displayed on the evaluation result information screen 30a and related information may be added.

[0120] In this way, by displaying evaluation result information that is different from the first evaluation information that experts refer to, it is possible to provide information that is easier for users and subjects to understand and use.

[0121] For ease of explanation, FIG. 7C illustrates the evaluation result information display area as four areas, 12a to 12d, but it may include fewer than four or five or more areas. In the example of FIG. 7C, only an image of the pharynx is displayed as the subject image because the possibility of influenza is being evaluated. However, in cases such as OHAT, where a comprehensive evaluation of the oral cavity is performed as the subject's condition, different images may be displayed for each area, such as an image of the lips, tongue, oral cavity interior, teeth, and gums. The measurement information display area may also include not only these image information but also information indicating at least one of the conditions indicated on the OHAT evaluation sheet, i.e., lips, sublingual, gums or oral mucosa, saliva, remaining teeth, dentures, oral hygiene, and toothache, at least one of the conditions of swallowing function and masticatory function, and at least one of the conditions of tongue movement, lip movement, oral moisture level, and mouth opening. Information on swallowing function, chewing function, tongue movement, lip movement, degree of oral moisture, and degree of mouth opening is information that is expected to have an impact on and correlate with the OHAT evaluation. Therefore, displaying this information together with information on the OHAT allows the user or subject to have a more appropriate understanding.

[0122] 7D is a diagram showing an example of an evaluation result information screen output by the measurement device 200-1 according to an embodiment of the present disclosure. Specifically, FIG. 7D is a diagram showing an example of an evaluation result information screen 30b, among the evaluation result information screens output via the output interface 214 in S22 of FIG. 4, in which the second evaluation information is generated by a specialist other than a doctor or a dentist. According to FIG. 7D, the evaluation result information screen 30b includes at least an evaluation result display area 31, measurement information display areas 32a to 32d, a medical interview information display area 33, a findings information display area 34, a specialist information display area 35, and a consultation recommendation area 38.

[0123] The evaluation result display area 31, measurement information display areas 32a to 32d, medical interview information display area 33, findings information display area 34, and expert information display area 35 are the same as those in the evaluation result information screen 30a of Figure 7C, and therefore their description will be omitted.

[0124] The consultation recommendation area 38 is an area displayed when the first expert is a specialist other than a doctor or dentist. This area includes information recommending the subject to visit a medical institution to which the doctor, dentist, or other specialist belongs (in the example of FIG. 7D , the information reads, "We recommend that you visit the following internist and be diagnosed with influenza."). Such consultation recommendation information is generated, for example, when generating the second evaluation information, by the processor 211 of the expert terminal device 200-2 accepting an operation input from the first expert via the input interface 213, and the first expert inputting a free-form sentence or inputting a fixed phrase by selecting an option. Alternatively, the processor 111 of the processing device 100 may generate the consultation recommendation information by referring to a previously stored consultation recommendation table and selecting an appropriate fixed phrase from the table based on the generated second evaluation information and evaluation information. Then, the processor 111 of the processing device 100 refers to the expert (evaluation) information and, if the first expert is other than a doctor or dentist, creates a consultation recommendation area 38 on the evaluation result information screen 30b and processes the generated information to be included in the consultation recommendation area 38.

[0125] Note that the information recommending a medical examination is exemplified by information recommending a medical examination at a medical institution to which a doctor or dentist belongs, as shown in Figure 7D, but information indicating that there is no need to visit such a medical institution may also be included in the information recommending a medical examination.

[0126] As described above, in FIGS. 7C and 7D, the evaluation result information is limited to providing medical advice when the first expert is at least one of a doctor and a dentist, and is limited to recommending medical consultation when the first expert is a specialist other than a doctor or a dentist. Therefore, the measurement device 200-1 itself may not be considered a medical device if it only measures measurement information without the purpose of diagnosis, allowing for more flexible operation. Furthermore, since the processing device 100 restricts access to only those authorized by the organization, it may not be considered a medical device, allowing for more flexible operation. Furthermore, in FIGS. 7C and 7D, the information displayed differs depending on whether the first expert is at least one of a doctor and a dentist, or someone else. This allows for flexible processing according to the expertise and qualifications of the first expert.

[0127] 4, the processor 211 of the measuring device 200-1 outputs the evaluation result information as shown in Figures 7C and 7D as the evaluation result of the subject's condition. This completes the processing sequence.

[0128] In this way, the output information from the trained evaluation model is not used as is to evaluate the subject's condition, but rather the evaluation result is generated after further evaluation by an expert. This allows for a more accurate evaluation. Furthermore, by matching the expert who evaluates the first evaluation information based on the expert's attribute information, etc., an appropriate evaluation can be made. Furthermore, by matching with an expert who can provide services such as diagnosis and care to improve and maintain the subject's condition, it becomes possible for both the user and the subject to maintain the subject's condition in a better condition.

[0129] Furthermore, according to this processing sequence, a first expert, whose access is managed by an organization, uses an evaluation result (first evaluation information) obtained by a processing device 100 managed by the same organization as the first expert to input second evaluation information, which is a diagnostic result. Evaluation result information obtained by processing the second evaluation information, which is the diagnostic result, is then transmitted to the subject. In other words, since the first expert uses a processing device 100 managed within the same organization and the subject simply receives the second evaluation information generated by the first expert, it is possible that the processing device 100 is not generally available. In this case, the processing device 100 may not be considered a medical device, allowing for more flexible operation.

[0130] 4, the first expert matched in S18 is a single expert, the second evaluation information in S18 is also a single piece of second evaluation information by the first expert, and the evaluation result information generated in T14 is generated based on a single piece of second evaluation information. However, the processor 111 may match multiple first experts in S18 and receive second evaluation information from each of the multiple first experts in S18. For example, if a single first expert is matched and the second evaluation information is not received, a second first expert is matched again, resulting in a loss of time. By matching multiple first experts as described above and enabling the reception of multiple pieces of second evaluation information, it is possible to quickly generate evaluation result information based on the latest second evaluation information, thereby reducing time loss and enabling the evaluation result information to be transmitted more quickly.

[0131] Furthermore, the processor 111 may generate evaluation result information from the plurality of pieces of second evaluation information generated by the plurality of first experts as described above at T14. For example, the processor 111 may generate evaluation result information from the average of the plurality of pieces of second evaluation information, or may generate evaluation result information from the most frequent evaluation (e.g., majority vote) among the plurality of pieces of second evaluation information. By generating evaluation result information in this manner, it is possible to generate evaluation result information with higher objectivity.

[0132] Furthermore, when acquiring a plurality of pieces of second evaluation information as described above in matching the second experts in S20 and S21, the processor 111 may take a majority vote and add points to the first expert who has made the most evaluations, making it more likely that the first expert will be matched as the second expert. In this way, it becomes possible to match an expert who can make a more accurate judgment as the second expert.

[0133] 5. Processing flow executed by the processing device 100 5A and 5B are diagrams illustrating a processing flow executed by the processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 5A illustrates a processing flow executed by the processing device 100 in steps S15 to S19 of the processing sequence of FIG. 4. FIG. 5B illustrates a processing flow executed in steps S20 and S21 of the processing sequence of FIG. 4. Each processing flow is mainly performed by the processor 111 of the processing device 100 by reading and executing a program stored in the memory 112. Note that the following describes a case in which a subject's condition is evaluated as "having a disease (e.g., influenza) in which findings are found in the subject's oral cavity" using a "subject image captured of at least a portion of the subject's oral cavity" as measurement information. However, similar processing is naturally possible even when other measurement information is used to evaluate other conditions.

[0134] (A) Processing flow executed in S15 to S19 in FIG. 4 5A, the processor 111 reads out the subject image stored in the subject management table based on the subject ID information of the subject received from the measurement device 200-1 (S111), and also reads out the evaluation information (S112). Note that, although the evaluation information is read out and used to generate the first evaluation information in the following, the evaluation information is not necessarily required, and only the subject image may be used.

[0135] Next, the processor 111 reads out the subject image and the evaluation information, and inputs this information into the trained evaluation model (S113). The trained evaluation model is a model used to evaluate the subject image for evaluating the state of the subject, and is a model generated by learning the training subject image and the training evaluation information based on information indicating the results of evaluating the state of the subject.

[0136] Here, Fig. 6 is a diagram showing a processing flow related to generation of a trained evaluation model according to an embodiment of the present disclosure. Specifically, Fig. 6 is a diagram showing a processing flow related to generation of a trained evaluation model used in S113 of Fig. 5. The processing flow may be executed by the processor 111 of the processing device 100, or may be executed by a processor of another device.

[0137] 6, the processor 111 executes a step of acquiring a learning subject image including at least a portion of the subject's oral cavity as the subject, and evaluation information such as interview information and findings information of the subject (S411). Next, the processor 111 executes a processing step of assigning learning evaluation result information indicating the results of an evaluation of the subject's condition as correct label information to the subject who is the subject of the subject image (S412). Here, the results of the evaluation of the subject's condition are obtained, for example, by conducting a test (for example, the results of a rapid influenza test using immunochromatography, a PCR test, or a virus isolation and culture test) or a diagnosis.

[0138] Then, the processor 111 executes a step of storing the assigned correct label information as evaluation result information for learning in association with the learning object image and the learning evaluation information (S413). Note that although the object image itself is used here, feature amounts obtained from the object image may also be used. Furthermore, the object image may be the image data itself captured by the measurement device 200-1, or may be image data after image processing such as sharpening has been performed on the image data.

[0139] Once the learning subject images, learning evaluation information, and corresponding correct label information are obtained, the processor 111 executes a step of performing machine learning of an evaluation pattern of the subject's condition using these (S414). As an example, the machine learning is performed by providing a set of this information to a neural network that combines neurons, and repeating learning while adjusting the parameters of each neuron so that the output from the neural network is the same as the correct label information. Then, a step of acquiring a trained evaluation model is executed (S415). The acquired trained evaluation model may be stored in the memory 112 of the processing device 100 or in another device connected to the processing device 100 via a wired or wireless network.

[0140] Here, the trained evaluation model can also be generated using machine learning techniques such as neural networks, convolutional neural networks, multi-layer neural networks (MLP), long short-term memory (LSTM), gated recurrent units (GRU), graph neural networks (GNN), and transformers; gradient boosting decision trees (GBDT) such as LightGBM (Light Gradient Boosting Machine), XGBoost, and CatBoost; ridge regression, logistic regression, support vector regression (SVR), nearest neighbor methods, decision trees, regression trees, and random forests.

[0141] Returning to FIG. 5A, the processor 111 inputs the subject image and evaluation information of the subject into the trained evaluation model generated as described above, thereby acquiring output information from the trained evaluation model (S114). After acquiring the output information, the processor 111 generates first evaluation information based on the acquired output information (S115). As an example of this process, the processor 111 generates the first evaluation information in a report format based on the acquired output information, the report including the input subject image, evaluation information, other attribute information, and a value, classification, or category indicating the subject's status output from the trained evaluation model. The processor 111 may also use, for example, a large-scale language model (LLM) in this process. The processor 111 generates a prompt for generating the first evaluation information based on the output information, the subject image, evaluation information, and other attribute information, and inputs the prompt into the large-scale language model. The processor 111 then acquires the first evaluation information as an output from the large-scale language model. The processor 111 stores the acquired first evaluation information in the subject management table in association with the subject ID information.

[0142] Next, the processor 111 executes a process for extracting a first expert to select an expert (first expert) who will evaluate the first evaluation information from among multiple experts who have been previously permitted to access the first evaluation information (S116). As an example of this process, the processor 111 references permission information from the expert management table to extract multiple experts who have been permitted to access the first evaluation information (experts whose permission information is stored with "permitted" associated with it). The processor 111 then executes the process by filtering experts selectable as the first expert based on predetermined information. This predetermined information may include, for example, at least one of the subject's attribute information and the first evaluation information. For example, the processor 111 references the subject's first evaluation information, and when the subject's condition is "possibility of influenza: yes" or "possibility of influenza: suspected," filters out experts who are qualified to diagnose and treat the subject's condition, such as doctors and nurses. When the subject's condition is "possibility of influenza: no," the processor 111 filters out experts other than the above experts, such as caregivers, care workers, and speech-language-hearing therapists. Therefore, the processor 111 can extract more appropriate experts by estimating the difficulty of the evaluation based on the first evaluation information and filtering based on the difficulty.

[0143] Furthermore, the predetermined information may be the accuracy of the first evaluation information acquired as output information in S114. For example, if the trained evaluation model evaluates the subject's condition as "possibly infected with influenza: yes," the trained evaluation model acquires an accuracy of "yes," and if the accuracy is close to 100%, it is determined to be "yes." On the other hand, if the accuracy is close to 0%, it is determined to be "no." In other words, if the condition is not close to either 100% or 0%, the difficulty of evaluation by the trained evaluation model is high, and it can be said that the trained evaluation model is in a state of uncertainty. Therefore, the processor 111 determines whether the accuracy is lower than a first threshold (e.g., "70%) and higher than a second threshold (a value lower than the first threshold, e.g., "30%)." When the processor 111 determines that the accuracy is lower than the first threshold and higher than the second threshold, it means that the difficulty of the evaluation is high, and therefore filters out professionals who are qualified to diagnose and treat oral conditions, such as doctors and nurses. On the other hand, when the processor 111 determines that the accuracy is equal to or higher than the first threshold or equal to or lower than the second threshold, it means that the difficulty of the evaluation is relatively low, and therefore filters out professionals other than the above professionals, such as caregivers, care workers, and speech-language-hearing therapists.

[0144] Next, the processor 111 calculates a priority score for each of the read experts and selects an expert (first expert) who will evaluate the first evaluation information (S117). As an example of this processing, the processor 111 reads attribute information of each expert and calculates a priority score by adding up points assigned to each attribute information or weighting the points. Then, the processor 111 selects one or more experts with a high priority score. The following information is an example of the attribute information used in this processing:

[0145] (Expert evaluation information) Based on information on the accuracy of other second evaluation information indicating the results of evaluating other first evaluation information, an expert with high accuracy is given a bonus based on the accuracy information of other second evaluation information. (This information is obtained by the processor 111, for example, by receiving feedback on the second evaluation information from the second expert when a diagnosis or care is performed by the second expert. Alternatively, the processor 111 selects multiple experts to evaluate the first evaluation information and obtains second evaluation information from each expert. The processor 111 then calculates the deviation between the average of the multiple pieces of second evaluation information obtained and the second evaluation information performed by each expert. Alternatively, the processor 111 selects multiple experts to evaluate the first evaluation information and obtains second evaluation information from each expert. The processor 111 then performs a majority vote among the multiple pieces of second evaluation information obtained and gives a bonus to the expert who provided the most evaluation results.) Based on information about the time it takes to transmit other second evaluation information to the processing device, an expert who takes a shorter time is given a bonus, or an expert who takes a longer time or does not transmit is deducted a bonus (this information is obtained by measuring the time from when the processor 111 transmits the first evaluation information to the expert until it receives the second evaluation information, and adding a bonus according to that time. Also, an expert who does not transmit second evaluation information despite the processor 111 transmitting first evaluation information is given a bonus, and this information is obtained by deducting a bonus). Based on the word-of-mouth information of each subject or user, experts with high word-of-mouth ratings are given additional points (this processing is performed by the processor 111 by performing language analysis on word-of-mouth information previously input by the subject or user in free text format, converting it into a score, and adding points according to the score. This processing is also performed by the processor 111 by acquiring the evaluation score of each expert by the user or subject, and adding points according to the evaluation score.)

[0146] (Operation information) Based on the schedule information, attendance information, and work schedule information of each expert, points are added to the experts who are currently working.

[0147] (Specialist Information) Based on the expert information of each expert, the expert who has a high degree of agreement with the subject's first evaluation information and evaluation information will be given additional points.

[0148] (location information) Based on the location information of each expert, points are added to experts who are located within a certain range of the target's location information.

[0149] The attribute information and the methods of adding, subtracting, and weighting points listed here are merely examples, and other attribute information of the experts or other attribute information of the subjects may be used. Furthermore, the experts to be selected may be a predetermined number of experts selected based on the priority score, or any predetermined number of experts selected in descending order of priority score.

[0150] Next, when a first expert is selected, the processor 111 transmits the first evaluation information of the subject to the expert terminal device 200-2 of the selected expert via the communication interface 113 (S118). Furthermore, when the transmission is performed, the processor 111 stores the time of transmission in association with the expert ID information of each expert.

[0151] Next, the processor 111 determines whether or not second evaluation information has been received from the expert terminal device 200-2 of one or more experts that transmitted the first evaluation information via the communication interface 113 (S119). If the result of this determination indicates that the second evaluation information has been received, the processor 111 stores the second evaluation information received from each expert in association with the subject ID information (S120).

[0152] On the other hand, if the second evaluation information is not received within a predetermined time, the processor 111 selects another first expert in the same manner as in S116 and S117 (S121). Then, the processor 111 executes the processing from S118 onwards again, such as transmitting the first evaluation information to the other selected first expert. This ends the processing flow.

[0153] (B) Processing flow executed in S20 and S21 of FIG. 4 According to FIG. 5B, the processor 111 reads out the second evaluation information stored in the subject management table (S211). Then, the processor 211 evaluates the expert (first expert) who transmitted the second evaluation information based on the read-out second evaluation information and the like, and generates expert evaluation information (S212). As an example of this processing, the processor 111 stores the time at which the second evaluation information was received from each expert, and calculates the elapsed time from the transmission time of the first evaluation information stored in S118 of FIG. 4. The processor 111 stores the calculated time as the expert evaluation information. Furthermore, when the processor 111 receives second evaluation information from multiple experts, the processor 111 calculates the average value of the received multiple pieces of second evaluation information. Then, the processor 111 calculates the deviation from the calculated average value for each piece of second evaluation information. The processor 111 stores the calculated value indicating the deviation as the expert evaluation information. 5A, if the processor 111 does not receive the second evaluation information within a predetermined time, the processor 111 stores the fact as expert evaluation information for the expert who did not send the second evaluation information. The stored expert evaluation information is used when calculating the priority score from the next time onward.

[0154] The processor 111 executes a process for extracting an expert (second expert) who can provide services such as diagnosis and care to the subject (S213). As an example of this process, the processor 111 executes the process by filtering experts selectable as the second expert based on predetermined information. At this time, the processing device 100 or information generated by the processing device 100 or information stored in the processing device 100 (e.g., first evaluation information) is not required for the second expert, so even an expert for which not only "permitted" but also "restricted" is stored as permission information in the expert management table may be selected. Furthermore, the predetermined information may include, for example, expert (evaluation) information from the attribute information of the subject. That is, the processor 111 refers to the expert (evaluation) information, reads out the expert ID information of the expert (first expert) who made the evaluation on the first evaluation information, and excludes the expert from the experts to be extracted in S213.

[0155] In addition, in this embodiment, the second expert is expected to, for example, visit the location of the subject and provide home visit medical care. Therefore, the predetermined information includes, for example, location information associated with the subject and location information of each expert. That is, the processor 111 references the location information associated with the subject's subject ID information and extracts experts whose location information is located within a predetermined range from the location information. Home visit medical care can only be provided by those located within a predetermined range from the subject, so it is possible to appropriately filter experts.

[0156] Furthermore, the predetermined information can be at least one of the subject's attribute information and the second evaluation information. For example, the processor 111 refers to the subject's second evaluation information, and when the subject's condition is "possibility of influenza: yes" or "possibility of influenza: suspected," the processor 111 filters out experts who are qualified to diagnose and treat the subject's condition, such as doctors and nurses, and when the subject's condition is "possibility of influenza: no," the processor 111 filters out experts other than the above experts, such as caregivers, care workers, and speech-language-hearing therapists. Thus, the processor 111 can appropriately filter out experts.

[0157] Next, the processor 111 reads out at least one of the expertise information, operation information, expert evaluation information, and other attribute information of each expert, and selects an expert who can provide services such as diagnosis and care to the subject (S214). As an example of this processing, the processor 111 selects one or more experts from among the multiple experts based on the expert's attribute information and the second evaluation information. Specifically, the processor 111 reads out the attribute information of each expert, and calculates a priority score by adding up or weighting the points assigned to each attribute information. Then, the processor 111 selects one or more experts with a high priority score. The following information is an example of the attribute information used in this processing:

[0158] (Expert evaluation information) Based on the accuracy information of the second evaluation information indicating the result of evaluating the first evaluation information, the expert with high accuracy is given a score (for example, the same process as that for the expert evaluation information in S117 is executed). Based on the information on the time it takes to transmit other second evaluation information to the processing device, the expert who takes a shorter time is given a bonus, or the expert who is slow or does not transmit is given a bonus. Based on the word-of-mouth information of each subject or user, experts with high word-of-mouth ratings are given additional points (this processing is performed by the processor 111 by performing language analysis on word-of-mouth information previously input by the subject or user in free text format, converting it into a score, and adding points according to the score. This processing is also performed by the processor 111 by acquiring the evaluation score of each expert by the user or subject, and adding points according to the evaluation score.)

[0159] (Operation information) Based on the schedule information, attendance information, and work schedule information of each expert, points are added to experts who are available on a specific date and time (for example, the date and time desired by the employer or the subject).

[0160] (Specialist Information) Based on the expert information of each expert, the experts who have a high degree of agreement with the subject's second evaluation information and evaluation information will be given additional points.

[0161] The attribute information and the methods of adding, subtracting, and weighting points listed here are merely examples, and other attribute information of the experts or other attribute information of the subjects may be used. Furthermore, the experts to be selected may be a predetermined number of experts selected based on the priority score, or any predetermined number of experts selected in descending order of priority score.

[0162] Next, when the processor 111 selects an expert (second expert), it reads out various attribute information associated with the second evaluation information, the expert (diagnosis, etc.) information, and the expert ID information, and generates evaluation result information. Then, the processor 111 transmits the generated evaluation result information to the measuring device 200-1 that transmitted the evaluation request via the communication interface 113 (S215).

[0163] The evaluation result information generated here is as shown in Figures 7C and 7D as an example, but may of course have other content. Furthermore, the processor 111 can also generate the evaluation result information by using, for example, a large-scale language model (LLM). The processor 111 generates a prompt for generating the evaluation result information based on various attribute information associated with the second evaluation information, expert (diagnosis, etc.) information, and expert ID information, and inputs the prompt to the large-scale language model. The processor 111 then obtains the evaluation result information as an output from the large-scale language model. This completes the processing flow.

[0164] As described above, in this embodiment, it is possible to provide a processing device, a processing program, a processing method, and a processing system that are capable of more appropriately evaluating the condition of a subject.

[0165] 5. Variations As described above, one embodiment according to the present disclosure has been described based on FIGS. 1 to 7D. However, various modified examples can be applied without being limited to those described above. Note that, although each modified example will be described below, it is also possible to use each modified example in appropriate combination. Furthermore, although details of the modified examples will be described below, the remaining parts can be implemented in the same manner as the embodiment described with reference to FIGS. 1 to 7D.

[0166] (A) Subject's condition In the above embodiment, the case where the subject's condition is evaluated as the possibility of the subject suffering from a disease that has been found in the oral cavity of the subject has been described. However, instead of or in addition to this, the condition of the oral cavity or other conditions of the subject can also be evaluated. The main processing proceeds in the same manner as the processing flow shown in FIG. 4 and the processing sequences shown in FIGS. 5A and 5B, except for the parts described below.

[0167] First, the trained evaluation model used to generate the first evaluation information is generated by assigning a correct label to a training subject image containing at least a portion of the subject's oral cavity as the subject, based on the evaluation results obtained by experts' evaluation (such as evaluation using the OHAT evaluation sheet).

[0168] The processor 111 of the processing device 100 executes a step of acquiring a learning subject image including the oral cavity of a subject. Next, the processor 111 executes a processing step of assigning information indicating the state of the oral cavity of the subject as a correct label to the subject who is the subject of the subject image.

[0169] Then, the processor 111 executes a step of storing the assigned correct label information as determination result information for learning in association with the subject image. Note that although the subject image itself is used here, feature amounts obtained from the subject image may also be used. Furthermore, the subject image may be the image data itself captured by the measurement device 200-1, or may be image data after image processing such as sharpening has been performed on the image data.

[0170] Once the learning subject images and the corresponding correct label information are obtained, the processor 111 executes a step of using these to perform machine learning of a determination pattern of the subject's oral condition (for example, lips, sublingual, gums or oral mucosa, saliva, remaining teeth, dentures, oral cleaning, toothache, etc., related to OHAT). As an example, this machine learning is performed by providing these sets of information to a neural network combining neurons, and repeating learning while adjusting the parameters of each neuron so that the output from the neural network is the same as the correct label information. Then, a step of acquiring a learned evaluation model is executed.

[0171] When the trained evaluation model is acquired as described above, the processor 111 of the processing device 100 uses the trained evaluation model in S113 to execute processing according to the processing flow shown in FIG. 5A.

[0172] 5B and displayed on the terminal device 200, similarly to FIGS. 7C and 7D, when the second evaluation information is generated by at least one of a doctor and a dentist, the evaluation result information screen may include an evaluation result display area, an evaluation information display area, an expert information display area, and a medical advice area 3. When the second evaluation information is generated by an expert other than a doctor or a dentist, the evaluation result information screen may include an evaluation result display area, an evaluation information display area, an expert information display area, and a consultation recommendation area 38.

[0173] (B) Evaluation Information In the above embodiment, the evaluation information is described as being based on medical interview information or findings input by a user or an expert or detected by another device. However, instead, the evaluation information may be obtained by image analysis based on subject images. For example, a trained analysis model can be obtained by providing a learning device with pairs of training subject images and correct label information that labels each training subject image based on the results of evaluation of each subject for the above-mentioned conditions, and repeating learning while adjusting the parameters of each neuron. Therefore, by inputting subject images into the trained analysis model, the processor 111 can obtain information indicating each of the above-mentioned conditions as output information.

[0174] (C) Billing process In the above embodiment, for example, FIG. 4 illustrates a case where a second expert is matched and information about the second expert is provided in the evaluation result information. In this case, the processor 111 can also perform billing processing on the expert terminal device 200-2 that can be used by the matched second expert. For example, when a second expert is selected through the matching processing (S21) of FIG. 4, the processor 111 transmits a payment request to the expert terminal device 200-2 of the second expert. Then, the processor 111 confirms that payment for the payment request has been confirmed or that payment information has been input (e.g., payment information input by credit card), and generates evaluation result information including attribute information of the second expert and transmits this to the measurement device 200-1 or the user terminal device. By performing billing processing for the provision of information in this way, the processing system 1 can more smoothly operate the service.

[0175] In the above embodiment, the subject or user acquires evaluation result information based on the subject's body measurement information. In this case, the processor 111 may perform a billing process on the user who made the evaluation request or the user who acquired the evaluation result information. For example, the processor 111 may transmit a payment request for a predetermined amount to the user terminal device before transmitting the evaluation request at T11 in FIG. 4 . The processor 111 then confirms that the payment for the payment request has been confirmed or that the payment information has been input (e.g., the payment information has been input by credit card), and then controls the screen to transition to a transmission screen for the evaluation request. Alternatively, the processor 111 may transmit evaluation result information at T14 in FIG. 4 , but transmit only a portion of the evaluation result information and a payment request for the predetermined amount for transmitting the remaining evaluation result information. The processor 111 then confirms that the payment for the payment request has been confirmed or that the payment information has been input (e.g., the payment information has been input by credit card), and then controls the screen to transmit the remaining evaluation result information.

[0176] (D) Payment of Remuneration In the above embodiment, FIGS. 1 to 7D illustrate a case where an expert selected as a first expert is easily extracted as an expert (second expert) providing services such as diagnosis and care by using a priority score or the like. However, in addition to this, it is also possible to pay a reward to the expert selected as a first expert each time the expert generates second evaluation information. Specifically, the processor 111 stores information indicating the number of cases in which second evaluation information has been transmitted within a predetermined period in the expert management table, calculates a reward amount according to the number of cases each time the period expires, and notifies each expert terminal device 200-2 of the reward amount. At this time, the processor 111 can also weight the reward amount by referring to the expert evaluation information of each expert. For example, the processor 111 can weight the reward amount according to the speed of transmission of the second evaluation information after transmitting an evaluation request for the first evaluation information. Furthermore, the processor 111 can weight the reward amount if the number of cases in which second evaluation information has been generated within a predetermined period exceeds a predetermined number.

[0177] The processor 111 may also rank each expert (e.g., assign a rank such as bronze, silver, gold, or platinum) according to the expert evaluation information of each expert. At this time, the processor 111 may transmit an account information screen on which the rank (bronze, silver, gold, or platinum) assigned to each expert is visible, for example, by receiving an information display request from the expert terminal device 200-2 of each expert. The processor 111 may also weight the amount of remuneration paid to each expert according to the rank. By visually checking the rank assigned to each expert, experts can be motivated to improve their skills and techniques in order to achieve a higher rank.

[0178] (E) Configuration of the Processing Device 100 FIG. 1 illustrates a configuration of the system 1 in which organization A restricts access to the processing device 100 or to information generated by or stored in the processing device 100. However, instead of this, organization A may restrict access to a portion of the processing device 100, or to a portion of the information generated by or stored in the processing device 100. FIG. 8 is a block diagram showing a configuration of the processing system 1 according to an embodiment of the present disclosure. According to FIG. 8, the processing device 100 is composed of two processing devices, a first processing device 100-1 and a second processing device 100-2. In this case, of the processes performed by the processing device 100 and the information generated or stored by the processing device 100, the processes related to S13 to S17 in FIG. 4 and the information used for those processes are performed, generated, or stored by the first processing device 100-1, and the other processes and the information used for those processes are performed, generated, or stored by the second processing device 100-2.

[0179] At this time, the second processing device 100-2 restricts access to the first processing device 100-1, or access to information generated by the first processing device 100-1 or information stored by the first processing device 100-1. That is, the second processing device 100-2 permits access to these only to those authorized in S06 of Fig. 4, and restricts access to other persons. This reduces the management burden on the entire processing device 100, enabling more flexible operation and management.

[0180] The processes and procedures described herein can be realized not only by those explicitly described in the embodiments, but also by software, hardware, or a combination thereof. Specifically, the processes and procedures described herein can be realized by implementing logic corresponding to the processes in media such as integrated circuits, volatile memory, nonvolatile memory, magnetic disks, and optical storage. Furthermore, the processes and procedures described herein can be implemented as computer programs and executed by various computers, including processing devices and server devices.

[0181] Although processes and procedures described herein are described as being performed by a single device, software, component, or module, such processes or procedures may be performed by multiple devices, multiple software, multiple components, and / or multiple modules. Furthermore, although various information described herein is described as being stored in a single memory or storage unit, such information may be stored in multiple memories within a single device or multiple memories distributed across multiple devices. Furthermore, software and hardware elements described herein may be realized by integrating them into fewer components or by decomposing them into more components. [Explanation of symbols]

[0182] 1 Processing System 100 Processing equipment 200-1 Measuring equipment 200-2 Expert terminal device

Claims

1. A processing device that restricts access to persons other than those previously authorized by an organization and includes at least one processor, the at least one processor: receiving measurement information obtained by measuring at least a part of the subject's body using a measurement device; acquiring first evaluation information indicating a result of evaluating the state of the subject by inputting the measurement information into a trained evaluation model for evaluating the state of the subject; transmitting the first evaluation information to an expert terminal device available to one or more first experts who have been previously authorized by the organization to access the first evaluation information; receiving second evaluation information from the expert terminal device, the second evaluation information being input by the one or more first experts and indicating a result of evaluating the first evaluation information; generating evaluation result information related to a result of evaluating the state based on the second evaluation information; a processing unit configured to perform processing for:

2. The processing device according to claim 1, wherein the trained evaluation model is generated by learning based on training measurement information obtained by measuring at least a portion of the subject's body and training evaluation information indicating the results of evaluating the subject's condition.

3. The processing device according to claim 1 , wherein the first evaluation information is acquired by inputting the measurement information and evaluation information associated with the subject into the trained evaluation model.

4. the subject's body includes at least a portion of the subject's oral cavity; The subject's condition is a disease manifested in the oral cavity. The processing device of claim 1 .

5. The processing device according to claim 1 , wherein the one or more first experts are selected from a plurality of experts based on attribute information of the plurality of experts and the first evaluation information.

6. The processing device according to claim 5 , wherein the attribute information of the experts includes expert evaluation information indicating an evaluation result for each expert.

7. The processing device described in claim 6, wherein the expert evaluation information includes at least one of the accuracy of other second evaluation information indicating the results of evaluating other first evaluation information, the time until the other second evaluation information is transmitted to the processing device, and the difficulty of evaluating the other first evaluation information.

8. The processing device according to claim 5 , wherein the attribute information of the experts further includes at least one of work information and expertise information of each expert.

9. The processing device according to claim 1 , wherein the evaluation result information includes attribute information of one or more second experts selected from the plurality of experts based on the second evaluation information and attribute information of each of the plurality of experts.

10. The processing device of claim 9 , wherein the second expert is a different expert from the first expert.

11. The first expert is at least one of a doctor and a dentist, The evaluation result information includes medical advice according to the subject's physical and mental condition. The processing device of claim 1 .

12. The first expert is a specialist other than a doctor or a dentist, The evaluation result information includes a recommendation to visit a medical institution. The processing device of claim 1 .

13. By being executed by at least one processor provided in a computer to which access is restricted to those other than those previously authorized by the organization, receiving measurement information obtained by measuring at least a part of the subject's body using a measurement device; acquiring first evaluation information indicating a result of evaluating the state of the subject by inputting the measurement information into a trained evaluation model for evaluating the state of the subject; transmitting the first evaluation information to an expert terminal device available to one or more first experts who have been previously authorized by the organization to access the first evaluation information; receiving second evaluation information from the expert terminal device, the second evaluation information being input by the one or more first experts and indicating a result of evaluating the first evaluation information; generating evaluation result information related to a result of evaluating the state based on the second evaluation information; A processing program that causes the at least one processor to function in such a manner.

14. A processing method executed by at least one processor included in a computer to which access is restricted to persons other than those previously authorized by an organization, the method comprising: receiving measurement information obtained by measuring at least a portion of the subject's body using a measurement device; acquiring first evaluation information indicating a result of evaluating the subject's condition by inputting the measurement information into a trained evaluation model for evaluating the subject's condition; transmitting the first evaluation information to an expert terminal available to one or more first experts who have been pre-authorized by the organization to access the first evaluation information; receiving second evaluation information from the expert terminal device, the second evaluation information being input by the one or more first experts and indicating a result of evaluating the first evaluation information; generating evaluation result information related to a result of evaluating the condition based on the second evaluation information; A processing method comprising:

15. a measurement device configured to measure measurement information of at least a portion of a subject's body; The processing device according to claim 1 , which is communicably connected to the measurement device; A processing system comprising:

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