Information processing system, information processing method, and program

Abstract

The present invention makes it possible to easily estimate the condition of a subject.　Provided is an information processing system characterized by comprising: an image acquisition unit that acquires a captured image of an eye of a subject; and an anterior chamber depth estimation unit that feeds the acquired captured image into a learning model learned through machine learning using eye images and anterior chamber depths as training data to estimate the anterior chamber depth of the subject.

Description

Information processing system, information processing method and program

[0001] The present invention relates to an information processing system, an information processing method, and a program.

[0002] Patent Document 1 discloses that the results of spectral analysis of an image of a subject's eye are used to diagnose cataracts.

[0003] Japanese Patent Application Laid-Open No. 2002-224041

[0004] However, in Patent Document 1, it takes time and effort to set the illumination light to be irradiated into the eye.

[0005] The present invention has been made in view of the above background, and aims to provide a technique that can easily estimate the condition of a subject.

[0006] The main invention of the present invention for solving the above-mentioned problems is an information processing system comprising: an image acquisition unit that acquires a photographed image of a subject's eye; and an anterior chamber depth estimation unit that estimates the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using the image of the eye and the anterior chamber depth as training data.

[0007] Other problems and solutions disclosed in this application will be made clear in the section on preferred embodiments of the invention and the drawings.

[0008] According to the present invention, the condition of a subject can be easily estimated.

[0009] FIG. 1 is a diagram illustrating an example of the overall configuration of an information processing system. FIG. 2 is a diagram illustrating an example of the hardware configuration of a subject terminal 1. FIG. 3 is a diagram illustrating an example of the software configuration of a subject terminal 1. FIG. 4 is a diagram illustrating an example of the hardware configuration of a management server 2. FIG. 5 is a diagram illustrating an example of the software configuration of a management server 2. FIG. 6 is a diagram explaining the operation of an information processing system. FIG. 7 is a diagram illustrating an example of the software configuration of a management server 2 according to a second embodiment. FIG. 8 is a diagram illustrating an example of the software configuration of a management server 2 according to a third embodiment.

[0010] <First Embodiment> A first embodiment of an information processing system will be described below. In the first embodiment, the anterior chamber depth (the distance from the cornea to the crystalline lens) of a subject is estimated from an image of the subject's eye. As the anterior chamber depth becomes shallower, the risk of acute glaucoma attacks and glaucoma attacks during a dilated eye examination increases. However, since it is possible to estimate the anterior chamber depth from an image of the subject's eye, it is also possible to grasp the risk of acute glaucoma attacks and glaucoma attacks during a dilated eye examination.

[0011] 1 is a diagram showing an example of the overall configuration of an information processing system. The information processing system of this embodiment is configured to include a management server 2. The management server 2 is communicably connected to a subject terminal 1 via a communication network. The communication network is, for example, the Internet, and is constructed using a public telephone network, a mobile phone network, a wireless communication path, Ethernet (registered trademark), etc.

[0012] The subject terminal 1 is a computer operated by the subject. The subject terminal 1 can be, for example, a smartphone, a tablet computer, or a personal computer. The subject terminal 1 is equipped with a camera (not shown) and can capture an image of the subject's face (especially the subject's eyes). The subject terminal 1 may be operated by a test cooperator rather than by the subject himself / herself.

[0013] The management server 2 may be a general-purpose computer such as a workstation or a personal computer, or may be logically realized by cloud computing.

[0014] <Subject Terminal 1> FIG. 2 is a diagram illustrating an example of the hardware configuration of the subject terminal 1. Note that the illustrated configuration is an example, and other configurations may also be used. The subject terminal 1 includes a CPU 101, a memory 102, a storage device 103, a communication interface 104, a touch panel display 105, and a camera 106. The storage device 103 stores various data and programs, such as a hard disk drive, a solid state drive, or a flash memory. The communication interface 104 is an interface for connecting to a communication network, such as an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or an RS232C connector for serial communication. The touch panel display 105 is an interface for inputting and outputting data, and can display images on the screen and acquire the position of a touch on the screen. The camera 106 can acquire captured images. Each functional unit of the subject terminal 1, which will be described later, is realized by the CPU 101 reading a program stored in the storage device 103 into the memory 102 and executing it, and each storage unit of the subject terminal 1 is realized as part of the storage area provided by the memory 102 and the storage device 103.

[0015] 3 is a diagram showing an example of the software configuration of the subject terminal 1. The subject terminal 1 includes an image acquisition unit 111 and an image transmission unit 112.

[0016] The image acquisition unit 111 acquires an image captured by the camera 106 (hereinafter referred to as a captured image). The image acquisition unit 111 can control the camera 106 by a known method to acquire the image captured by the camera 106. When capturing an image with the camera 106, the image acquisition unit 111 can, for example, output a message to the subject instructing the subject to capture an image of his or her eyes. The image acquisition unit 111 may, for example, be activated by the subject to acquire an image, or may acquire a captured image in response to receiving a message instructing capture from the management server 2.

[0017] The image acquiring unit 111 may be configured to accept a designation of a photographed image taken in advance. For example, the image acquiring unit 111 may accept a designation of an image of the subject's eye from among images registered in an image storage unit such as a camera roll, or may read out the photographed image from a storage device in which a file is stored (a storage device provided in the subject terminal 1 or a storage medium connected to the subject terminal 1, or a storage device provided in an external server) in response to a designation from the subject.

[0018] It is assumed that the subject's eyes are captured in the captured image. The captured image may be a photograph of the subject's face, or may be a photograph of only the area around the subject's eyes. The image acquisition unit 111 may determine whether the eyes are included in the captured image. For example, the image acquisition unit 111 may provide the captured image to a model for eye detection and determine whether the eyes are included in the captured image based on whether the eyes can be detected from the captured image. If the eyes are not included, the image acquisition unit 111 may output a message to the subject to retake the captured image, and may acquire another captured image captured by the camera 106 (or a captured image captured in advance).

[0019] The image transmission unit 112 transmits the captured image acquired by the image acquisition unit 111 to the management server 2 .

[0020] FIG. 4 is a diagram illustrating an example of the hardware configuration of the management server 2. Note that the illustrated configuration is an example, and other configurations may also be used. The computer includes a CPU 201, memory 202, storage device 203, communication interface 204, input device 205, and output device 206. The storage device 203 stores various data and programs, and is, for example, a hard disk drive, solid state drive, or flash memory. The communication interface 204 is an interface for connecting to a communication network, and is, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or RS232C connector for serial communication. The input device 205 is, for example, a keyboard, mouse, touch panel, button, microphone, or the like for inputting data. The output device 206 is, for example, a display, printer, speaker, or the like for outputting data. Each functional unit of the management server 2 described below is realized by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it, and each storage unit of the management server 2 is realized as part of the storage area provided by the memory 202 and the storage device 203.

[0021] 5 is a diagram showing an example of the software configuration of the management server 2. The management server 2 includes a learning model storage unit 231, a subject information storage unit 232, an image acquisition unit 211, a subject information output unit 213, a subject information acquisition unit 214, and an anterior chamber depth estimation unit 215.

[0022] The learning model storage unit 231 stores a learning model for estimating the anterior chamber depth of a subject. The learning model stored in the learning model storage unit 231 can be created by machine learning using, for example, an image of the eye and the anterior chamber depth as training data. The learning model can also be created by learning using machine learning using an image of the eye, the anterior chamber depth, and at least one of age and gender as training data. The learning model may be updated by machine learning upon receiving feedback of a captured image of the subject's eye and the anterior chamber depth of the subject (for example, the anterior chamber depth measured by the subject using a separate device can be received from the subject terminal 1). Note that the learning model storage unit 231 may not be included in the management server 2 but may be included in an external server, and the learning model may be used via an API provided by the external server.

[0023] The subject information storage unit 231 stores subject information related to the subject. The subject information storage unit 231 stores the subject information. The subject information may include at least one of the subject's age and sex. The subject information may include any attribute of the subject, such as name and address. The subject information may also include an estimated value of the anterior chamber depth estimated by the anterior chamber depth estimation unit 214, which will be described later.

[0024] The image acquisition unit 211 acquires a captured image of the subject's eye. The image acquisition unit 211 can receive data of the captured image transmitted from the subject terminal 1. The image acquisition unit 211 can transmit a message to the subject terminal 1 instructing it to capture an image of the subject's eye. The subject terminal 1 can capture an image in response to the message and receive the captured image of the eye from the subject terminal 1.

[0025] The subject information acquisition unit 214 acquires subject information. The subject information acquired by the subject information acquisition unit 214 does not need to include the anterior chamber depth. The subject information acquisition unit 214 can receive values ​​of each item included in the subject information, such as the subject's name, address, age, and gender, from the subject terminal 1, and register them as subject information in the subject information storage unit 231. The subject information can also include the refractive values ​​of the subject's eyes (spherical power, cylindrical power, spherical equivalent power, astigmatism axis, etc.).

[0026] The anterior chamber depth estimation unit 215 estimates the anterior chamber depth of the subject. The anterior chamber depth estimation unit 215 can estimate the anterior chamber depth of the subject by providing the acquired captured image to a learning model stored in the learning model storage unit 231.

[0027] The anterior chamber depth estimation unit 215 may estimate the anterior chamber depth of the subject by providing the acquired photographed image and at least one of the items of the subject information stored in the subject information storage unit 231 to the learning model stored in the learning model storage unit 231. For example, the anterior chamber depth estimation unit 215 can estimate the anterior chamber depth of the subject by providing the acquired photographed image and at least one of the age and gender of the subject information to the learning model stored in the learning model storage unit 231. Furthermore, for example, the anterior chamber depth estimation unit 215 can estimate the anterior chamber depth of the subject by providing the acquired photographed image and a refraction value of the subject information to the learning model stored in the learning model storage unit 231.

[0028] The subject information output unit 213 outputs information about the subject (hereinafter referred to as subject information). The subject information may include information identifying the subject and the estimated anterior chamber depth of the subject. The subject information output unit 213 may transmit the subject information to the subject terminal 1, output the subject information to an output device such as a display, or transmit the subject information to a terminal (not shown) of a medical institution staff member such as an ophthalmologist.

[0029] <Operation> FIG. 6 is a diagram illustrating the operation of the information processing system.

[0030] The subject operates the subject terminal 1 to photograph his or her eyes (S301), and the subject terminal 1 transmits the photographed image to the management server 2 (S302).

[0031] The management server 2 estimates the anterior chamber depth of the subject by applying the captured image received from the subject terminal 1 to the learning model stored in the learning model storage unit 231 (S303). The management server 2 can create and output subject information including the anterior chamber depth of the subject (S304).

[0032] As described above, the information processing system according to the first embodiment can estimate the anterior chamber depth of a subject from an image of the subject's eye, which can be used to assess the risk of glaucoma.

[0033] The management server 2 may include a glaucoma attack risk determination unit that determines the risk of an acute glaucoma attack or a glaucoma attack during a dilated eye examination based on the estimated anterior chamber depth. In this case, the subject information output unit 214 can output the risk of glaucoma together with or instead of the estimated value of the anterior chamber depth.

[0034] <Disclosure> The present disclosure also includes the following configurations. [Item 1] An information processing system comprising: an image acquisition unit that acquires a captured image of a subject's eye; and an anterior chamber depth estimation unit that estimates the anterior chamber depth of the subject by providing the acquired captured image to a learning model that has been trained by machine learning using the image of the eye, the anterior chamber depth, and at least one of the age and the gender. [Item 2] The information processing system according to Item 1, further comprising: a subject information storage unit that stores at least one of the age and gender of the subject, the learning model having been trained by machine learning using the image of the eye, the anterior chamber depth, and at least one of the age and the gender as training data, and the anterior chamber depth estimation unit that estimates the anterior chamber depth of the subject by providing the acquired captured image and at least one of the age and the gender stored in the subject information storage unit to the learning model. [Item 3] The information processing system according to Item 1, further comprising: a subject information storage unit that stores a refractive value of the eye of the subject, wherein the learning model is learned by machine learning using an image of the eye, the anterior chamber depth, and the refractive value as training data, and an anterior chamber depth estimation unit that estimates the anterior chamber depth of the subject by providing the acquired photographed image and the refractive value stored in the subject information storage unit to the learning model. [Item 4] An information processing method, characterized in that a computer executes the steps of: acquiring a photographed image of the eye of the subject, and estimating the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been learned by machine learning using an image of the eye and the anterior chamber depth as training data. [Item 5] A program for causing a computer to execute the steps of: acquiring a photographed image of a subject's eye; and estimating the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using the eye image and the anterior chamber depth as training data.

[0035] Second Embodiment Next, a third embodiment of the information processing system will be described. In the third embodiment, the degree of cataract of a subject is estimated from an image of the subject's eye.

[0036] 7 is a diagram showing an example of the software configuration of the management server 2 according to the second embodiment. In the second embodiment, the management server 2 includes a learning model storage unit 231, a subject information storage unit 232, an image acquisition unit 211, a subject information output unit 213, a subject information acquisition unit 214, and an opacity estimation unit 216. Compared to the configuration of the management server 2 according to the first embodiment, the anterior chamber depth estimation unit 215 is omitted, and the opacity estimation unit 216 is added. Note that in the second embodiment, the management server 2 may also include the anterior chamber depth estimation unit 215. Below, differences from the first embodiment will be mainly described.

[0037] The learning model stored in the learning model storage unit 231 in the second embodiment can be created by learning through machine learning using eye images and the degree of cataract as training data. The learning model can also be created by learning through machine learning using eye images and the anterior chamber depth as training data. The learning model can also be created by learning through machine learning using eye images, the anterior chamber depth, and at least one of age and gender as training data.

[0038] The subject information stored in the subject information storage unit 231 may also include an estimated value of the degree of cataract estimated by the opacity estimation unit 216 (described later).

[0039] The subject information output by the subject information output unit 213 includes the estimated degree of cataract.

[0040] The subject information acquisition unit 214 acquires subject information. The subject information acquired by the subject information acquisition unit 214 does not need to include the degree of cataract. The subject information acquisition unit 214 can receive values ​​of each item included in the subject information, such as the subject's name, address, age, and gender, from the subject terminal 1, and register them as subject information in the subject information storage unit 231. The subject information can also include the refractive values ​​of the subject's eyes (spherical power, cylindrical power, spherical equivalent power, astigmatism axis, etc.).

[0041] The opacity estimation unit 216 estimates the degree of cataract (opacity degree) of the subject. The opacity estimation unit 216 can estimate the degree of cataract of the subject by providing the acquired captured image to a learning model stored in the learning model storage unit 231.

[0042] The opacity estimation unit 216 may estimate the degree of cataract of the subject by providing the acquired photographed image and at least one of the items of the subject information stored in the subject information storage unit 231 to the learning model stored in the learning model storage unit 231. For example, the opacity estimation unit 216 can estimate the degree of cataract of the subject by providing the acquired photographed image and at least one of the age and gender of the subject information to the learning model stored in the learning model storage unit 231.

[0043] As described above, the information processing system according to the second embodiment can estimate the degree of cataract of a subject from a captured image of the subject's eye.

[0044] The disclosure according to the second embodiment may include the following configuration: [Item 1] An information processing system comprising: an image acquisition unit that acquires a captured image of a subject's eye; and an opacity estimation unit that estimates the degree of cataract of the subject by providing the acquired captured image to a learning model that has been trained by machine learning using the eye image, the degree of cataract, and at least one of the age and the gender as training data. [Item 2] The information processing system according to item 1, further comprising: a subject information storage unit that stores at least one of the age and gender of the subject, wherein the learning model has been trained by machine learning using the eye image, the degree of cataract, and at least one of the age and the gender as training data, and the anterior chamber opacity estimation unit estimates the degree of cataract of the subject by providing the acquired captured image and at least one of the age and the gender stored in the subject information storage unit to the learning model. [Item 3] An information processing method, characterized in that a computer executes the steps of: acquiring photographed images of a subject's eyes; and estimating the degree of cataract of the subject by providing the acquired photographed images to a learning model trained by machine learning using the eye images and the degree of cataract as training data. [Item 4] A program for causing a computer to execute the steps of: acquiring photographed images of a subject's eyes; and estimating the degree of cataract of the subject by providing the acquired photographed images to a learning model trained by machine learning using the eye images and the degree of cataract as training data.

[0045] Third Embodiment Next, a third embodiment of the information processing system will be described. In the third embodiment, the density of corneal endothelial cells is estimated from an image of the eye of a subject.

[0046] 8 is a diagram showing an example of the software configuration of the management server 2 according to the third embodiment. In the third embodiment, the management server 2 includes a learning model storage unit 231, a subject information storage unit 232, an image acquisition unit 211, a subject information output unit 213, a subject information acquisition unit 214, and a corneal endothelial cell density estimation unit 217. Compared to the configuration of the management server 2 according to the first embodiment, the anterior chamber depth estimation unit 215 is omitted, and the corneal endothelial cell density estimation unit 217 is added. Note that in the third embodiment as well, the management server 2 may include the anterior chamber depth estimation unit 215 and the opacity estimation unit 216.

[0047] The learning model stored in the learning model storage unit 231 in the third embodiment can be created by learning through machine learning using an image of the eye and the density of corneal endothelial cells (hereinafter referred to as corneal endothelial cell density) as training data. The learning model can also be created by learning through machine learning using an image of the eye, the corneal endothelial cell density, and at least one of age and gender as training data.

[0048] The subject information stored in the subject information storage unit 231 can also include an estimated value of the corneal endothelial cell density estimated by the corneal endothelial cell density estimation unit 217, which will be described later.

[0049] The subject information output by the subject information output section 213 includes the estimated corneal endothelial cell density.

[0050] The subject information acquisition unit 214 acquires subject information. The subject information acquired by the subject information acquisition unit 214 does not need to include corneal endothelial cell density. The subject information acquisition unit 214 can receive values ​​of each item included in the subject information, such as the subject's name, address, age, and gender, from the subject terminal 1, and register them as subject information in the subject information storage unit 231. The subject information can also include the refractive values ​​of the subject's eyes (spherical power, cylindrical power, spherical equivalent power, astigmatism axis, etc.).

[0051] The corneal endothelial cell density estimation unit 217 estimates the corneal endothelial cell density of the subject. The corneal endothelial cell density estimation unit 217 can estimate the corneal endothelial cell density of the subject by providing the acquired photographed image to a learning model stored in the learning model storage unit 231.

[0052] The corneal endothelial cell density estimation unit 217 may estimate the corneal endothelial cell density of the subject by providing the acquired photographed image and at least any one of the items of the subject information stored in the subject information storage unit 231 to the learning model stored in the learning model storage unit 231. For example, the corneal endothelial cell density estimation unit 217 can estimate the corneal endothelial cell density of the subject by providing the acquired photographed image and at least any one of the age and gender of the subject information to the learning model stored in the learning model storage unit 231.

[0053] As described above, according to the information processing system of the third embodiment, it is possible to estimate the corneal endothelial cell density of a subject from a photographed image of the subject's eye.

[0054] Incidentally, the subject information may be configured to manage information indicating whether or not the subject wears contact lenses, and a message sending unit may be provided that sends a message to subjects who wear contact lenses, urging them to have a corneal endothelial cell density test. The message sending unit may send the message periodically, or after a predetermined period of time has passed since the user's registration or the previous corneal endothelial cell density test, to urge the subject to periodically understand the state of the corneal endothelial cell density.

[0055] The disclosure according to the third embodiment may include the following configurations. [Item 1] An information processing system comprising: an image acquisition unit that acquires captured images of a subject's eye; and a corneal endothelial cell density estimation unit that estimates the corneal endothelial cell density of the subject by providing the acquired captured images to a learning model that has been trained by machine learning using the eye images and the corneal endothelial cell density as training data. [Item 2] The information processing system according to item 1, further comprising: a subject information storage unit that stores at least one of the age and gender of the subject, wherein the learning model has been trained by machine learning using the eye images, the corneal endothelial cell density, and at least one of the age and the gender as training data, and wherein the anterior chamber corneal endothelial cell density estimation unit estimates the corneal endothelial cell density of the subject by providing the acquired captured images and at least one of the age and the gender stored in the subject information storage unit to the learning model. [Item 3] An information processing method, characterized in that a computer executes the steps of: acquiring a photographed image of a subject's eye; and estimating the corneal endothelial cell density of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using the eye image and the corneal endothelial cell density as training data. [Item 4] A program for causing a computer to execute the steps of: acquiring a photographed image of a subject's eye; and estimating the corneal endothelial cell density of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using the eye image and the corneal endothelial cell density as training data.

[0056] Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention.

[0057] 1 Subject terminal 2 Management server

Claims

1. an image acquisition unit that acquires an image of the subject's eye; an anterior chamber depth estimation unit that estimates the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using an image of the eye and the anterior chamber depth as training data; a subject information storage unit that stores at least one of the age and sex of the subject; Equipped with the learning model is learned by machine learning using the image of the eye, the anterior chamber depth, and at least one of the age and the gender as training data; the anterior chamber depth estimation unit estimates the anterior chamber depth of the subject by providing the acquired photographed image and at least one of the age and the sex stored in the subject information storage unit to the learning model; An information processing system characterized by:

2. an image acquisition unit that acquires an image of the subject's eye; an anterior chamber depth estimation unit that estimates the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using an image of the eye and the anterior chamber depth as training data; a subject information storage unit that stores the refractive value of the subject's eye; Equipped with the learning model is learned by machine learning using the image of the eye, the anterior chamber depth, and the refraction value as training data; the anterior chamber depth estimation unit estimates the anterior chamber depth of the subject by providing the acquired photographed image and the refractive value stored in the subject information storage unit to the learning model; An information processing system characterized by:

3. acquiring an image of the subject's eye; a step of estimating the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using an image of the eye and the anterior chamber depth as training data; storing at least one of the age and sex of the subject in a subject information storage unit; The computer executes the learning model is learned by machine learning using the image of the eye, the anterior chamber depth, and at least one of the age and the gender as training data; an information processing method, characterized in that, in the step of estimating the anterior chamber depth, the computer estimates the anterior chamber depth of the subject by providing the acquired photographed image and at least one of the age and the gender stored in the subject information storage unit to the learning model.

4. acquiring an image of the subject's eye; a step of estimating the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using an image of the eye and the anterior chamber depth as training data; storing the refractive value of the eye of the subject in a subject information storage unit; Equipped with the learning model is learned by machine learning using the image of the eye, the anterior chamber depth, and the refraction value as training data; In the step of estimating the anterior chamber depth, the computer estimates the anterior chamber depth of the subject by providing the acquired photographed image and the refractive value stored in the subject information storage unit to the learning model; An information processing method comprising:

5. acquiring an image of the subject's eye; a step of estimating the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using an image of the eye and the anterior chamber depth as training data; storing at least one of the age and sex of the subject in a subject information storage unit; A program for causing a computer to execute the above, the learning model is learned by machine learning using the image of the eye, the anterior chamber depth, and at least one of the age and the gender as training data; in the step of estimating the anterior chamber depth, causing the computer to estimate the anterior chamber depth of the subject by providing the acquired photographed image and at least one of the age and the sex stored in the subject information storage unit to the learning model; A program characterized by.

6. acquiring an image of the subject's eye; a step of estimating the anterior chamber depth of the subject by providing the acquired photographed image to a learning model that has been trained by machine learning using an image of the eye and the anterior chamber depth as training data; storing the refractive value of the eye of the subject in a subject information storage unit; A program for causing a computer to execute the above, the learning model is learned by machine learning using the image of the eye, the anterior chamber depth, and the refraction value as training data; in the step of estimating the anterior chamber depth, causing the computer to estimate the anterior chamber depth of the subject by providing the acquired photographed image and the refractive value stored in the subject information storage unit to the learning model; A program characterized by.