JP7467830B2 - Visual simulation method and visual simulation program - Google Patents

Description

This disclosure relates to a visual simulation method and a visual simulation program.

There are known eye diseases, such as glaucoma, that can impair the field of vision. The progression of the disease can be examined using perimeters and examination devices such as OCT (optical coherence tomography).

In recent years, methods have been proposed for estimating a patient's future visual field. For example, the method disclosed in Patent Document 1 estimates a patient's future visual field based on the results of multiple perimeter tests.

JP 2014-166250 A

Visual field disorders often progress gradually, without the patient realizing it. Furthermore, visual field disorders are irreversible. Therefore, in order to prevent the progression of disorders and prevent blindness, it is important for patients to be proactive in their treatment and to make appropriate visits to hospitals and take medication.

However, it is difficult for patients to recognize symptoms, especially in the early stages of visual field impairment. In addition, because visual field impairment progresses slowly and is irreversible, it is difficult for patients to realize the effects of treatment. Therefore, the inventors focused on and investigated methods to effectively motivate patients to undergo treatment.

This disclosure was made in light of the above circumstances, and its technical objective is to effectively motivate patients to undergo eye treatment.

A visual simulation method according to a first aspect of the present disclosure includes an estimated information acquisition step in which a computer acquires estimated information indicating a visual state of a patient's eye at a certain time point, and an output step in which the computer outputs a simulation image indicating how the patient's eye looks at the certain time point, the simulation image corresponding to the estimated information, wherein the estimated information acquisition step acquires the estimated information for each of the left and right patient's eyes, and the output step outputs the simulation images corresponding to the estimated information for each of the left and right patient's eyes for presentation to the left and right patient's eyes individually . Furthermore, the estimated information acquisition step acquires, as the estimated information indicating the visual state at a certain time point in the future, first estimated information when the progression speed of the impairment is a first speed, and second estimated information when the progression speed of the impairment is a second speed different from the first speed, and the computer generates a first simulation image based on the first estimated information and a second simulation image based on the second estimated information as the simulation images having different progression speeds of the impairment for the same visual field .

The visual simulation program according to the second aspect of the present disclosure causes the computer to execute the visual simulation method according to the first aspect.

The present disclosure makes it possible to effectively motivate patients to undergo eye treatment.

1 is a diagram showing an overview of an ophthalmologic system according to a first embodiment. FIG. 2 is a diagram showing a detailed configuration of a patient terminal. 1 is a diagram for explaining the flow of a visual simulation method in an embodiment. 1 is a graph showing the change in MD value over time when the progression rates of the disorder are different.

The present disclosure will be described below based on an embodiment with reference to the drawings.

Figure 1 shows a schematic configuration of an ophthalmic system 1 in the first embodiment. The ophthalmic system 1 is used to present to the patient's eye a simulation image showing how the patient's eye sees at a certain point in time. The certain point in time here may be a point in time other than the present, or may be a point in the past or future. It may also be a point in the past, present, or future. In the following description, the ophthalmic system 1 is used, as an example, to present a simulation image corresponding to a future point in time.

The ophthalmologic system 1 shown in FIG. 1 includes an eye examination device 10, a PC 20, and a patient terminal 30. Each device may be connected to each other via a network.

<Eye Examination Device>
The eye examination apparatus 10 is used to examine the visual function of a patient's eye.

In the first embodiment, retinal function is mainly examined as visual function. The eye examination device 10 may be used to examine the retinal visual sensitivity for each part. Such an eye examination device 10 may be, for example, a perimeter, an ERG (electroretinogram), or an FRG (functional retinography). The perimeter may be a static perimeter (e.g., a Humphrey perimeter and a microperimeter, etc.) or a kinetic perimeter (e.g., a Goldmann perimeter, etc.).

In recent years, a method of estimating a patient's visual condition based on structural characteristics of the retina photographed by a fundus imaging device has been attracting attention. Fundus imaging devices that can be used for such a method include OCT, fundus cameras, and SLOs. The fundus imaging device may be a device that photographs the fundus at a cellular level. As an example of a method of estimating a patient's visual condition based on structural characteristics of the retina, the following document proposes a method of estimating a patient's visual field from retinal layer thickness information (layer thickness distribution information) measured by an optical coherence tomography (OCT).
Sugiura, H. et al. (2018) Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18. New York, New York, USA: ACM Press, pp. 783-792.

Unless otherwise specified, the following description assumes that the eye examination device 10 is a perimeter and that the examination results obtained by the eye examination device 10 are information indicating the visual sensitivity of the retina at each location.

<Computer>
In the first embodiment, a visual simulation program is executed by the PC 20. In the first embodiment, a simulation image is generated by the PC 20 and output to a display device (a patient terminal in the first embodiment). The simulation image is an image showing how the patient's eye sees at a certain point in time, and in this embodiment, how the patient will see in the future. In the first embodiment, the simulation image is described as being generated by image processing by the PC 20.

In the first embodiment, the PC 20 includes a processor 21 (processing device). In the first embodiment, the processor 21 also functions as an image processor. The PC 20 may receive operational input from an examiner or the like via a user interface (not shown) included in the PC 20. For example, the PC 20 may receive information indicating the time point at which the appearance is to be simulated (e.g., the age of the patient) via the user interface, and a simulation image at the received "time point" may be generated by each process described below.

In the first embodiment, the visual simulation program may be stored in a non-volatile memory (e.g., memory 22) of the PC 20 that is accessible from the processor 21.

<Patient terminal>
In the first embodiment, the patient terminal 30 is used as a display device. That is, a simulation image is presented to the patient's eye by the patient terminal 30. In the first embodiment, the patient terminal 30 can simultaneously present individual images to the left and right patient's eyes. That is, different simulation images can be presented between the left and right eyes to be examined.

The patient terminal 30 may be a wearable device, and as an example, may be a head-mounted display as shown in Figures 1 and 2. For ease of explanation, the patient terminal 30 will be described below as having a screen 31 that allows the patient to observe images. In the first embodiment, the screen 31 is divided into two areas, left and right. The left half of the screen 31 (the left half as seen by the patient) is used to present an image to the left eye, and the right half of the screen 31 (the right half as seen by the patient) is used to present an image to the right eye.

However, this is not necessarily limited to this, and the patient terminal 30 may be equipped with a projector that projects an image directly onto the retina of the patient's eye. In this case, the projector may project an image separately onto each of the patient's left and right eyes. As a specific example, one projector may be provided for each of the patient's left and right eyes. When the patient terminal 30 is a head-mounted display, the patient terminal 30 may be a portable computer such as a smartphone or tablet attached to a headset, or may be a standalone device.

The patient terminal 30 may also include a diopter correction unit. The diopter correction unit may have a correction lens 33 disposed between the screen 31 and the patient's eye. The diopter correction unit is used to correct refractive errors in the patient's eye. The diopter correction unit may include a mechanism for moving the position of the screen 31 or the correction lens in the forward and backward directions.

<Operation description>
Next, a visual simulation method using the ophthalmologic system 1 in the first embodiment will be described with reference to FIGS.

<Inspection steps>
The eye examination device 10 performs a number of tests on the visual function of the patient's eye in advance. Each test is preferably performed at intervals of several months. In order to estimate the state of the visual function (visual state) of the patient's eye at a certain point in time (here, a certain point in time in the future) using, for example, the estimation processing method disclosed in the above-mentioned Patent Document 1, it is preferable to perform the test three or more times.

In the first embodiment, the eye examination device 10 performs tests on the visual function of the left and right patient's eyes multiple times in advance. As a result, data showing the distribution of visual sensitivity in each of the patient's left and right eyes is obtained in each test.

The data showing the visibility distribution is transferred from the eye examination device 10 to the PC 20 and stored in the memory (e.g., memory 22) of the PC 20. In the first embodiment, in this state, the visual simulation program is executed by the PC 20.

<Estimated information acquisition step>
First, the PC 20 acquires estimated information. The estimated information indicates the visual state of the patient's eye at a certain time (here, a future time). In the first embodiment, as shown in FIG. 3, estimated information is acquired for each of the left and right patient's eyes. The estimated information may indicate the visual state at a predetermined time (e.g., one year later, three years later, ten years later, etc.), or may indicate the visual state at a desired time of the examiner or the patient. The desired time may be set in advance based on an operation input to an input interface, for example. In addition, multiple pieces of estimated information indicating visual states at multiple future time points may be acquired. Note that, in this case, it refers to putting the estimated information in a state that can be processed by the processor 21 of the PC 20. For example, the estimated information is acquired by storing the estimated information in a predetermined memory area accessible by the processor 21.

In the first embodiment, an estimation process based on past test results (also called "prognosis estimation") is executed by the processor 21 of the PC 20, and as a result, estimated information is obtained. In the estimation process, the future visual condition of the patient's eye is estimated based on the results of multiple past tests.

For example, the processing contents of Patent Document 1 may be applied to the estimation process. In this case, for example, the processor 21 may estimate data indicating the retinal visibility of each part at the future (or any time point) by Bayesian estimation based on data indicating the retinal visibility of each part of the patient's eye obtained from multiple past examinations. Furthermore, the estimation method is not necessarily limited to Bayesian estimation, and other estimation methods may be used.

The processor 21 may also estimate data indicating the visual condition in the future (or at any time) (e.g., data indicating the retinal visual sensitivity of each part) from multiple past test results in light of the machine learning learning model. In this case, the learning model may be formed, for example, based on multiple test results for each of multiple patient's eyes. The learning model may be formed based on test results accumulated over several years to several decades of follow-up observation for each patient's eye.

<Image processing steps>
In the first embodiment, the processor 21 performs image processing on the input image based on the estimated information, and as a result, a simulation image is generated. The simulation image is an input image processed by image processing, and is an image showing how the input image will be seen by the patient's eye in the future. The processor 21 processes the input image to hide areas with poor visual condition (in other words, processes the areas to make them difficult to see), thereby generating the simulation image.

In the first embodiment, as shown in FIG. 3, a simulation image (L) for the left eye is generated based on estimated information (L) for the left eye, and a simulation image (R) for the right eye is generated based on estimated information (R) for the right eye.

In addition, simulation images corresponding to multiple future time points are generated based on each estimated information.

The input image is, for example, an image showing the outside world, and may be, for example, an image showing a landscape as shown in FIG. 3. It may also be an image of a person or an animal. The input image may be a still image or a video. If it is a video, it may be captured in real time. In this case, for example, the patient terminal 30 may further be provided with an outside camera for capturing the input image and the video in real time. Also, there may or may not be parallax between the input image for the left eye and the input image for the right eye.

Each area in the input image can be associated with each area in the visual field that corresponds to the estimated information (in other words, each part of the retina) on a one-to-one basis. Using this correspondence, the processor 21 uses image processing to process the input image in a way that hides areas of poor visual condition (in other words, makes them difficult to see), and generates a simulation image.

In the first embodiment, for example, the worse the visual condition of each region in the input image (here, the lower the retinal visibility), the more difficult it is to see through image processing. In this case, each region of the input image may be divided into three or more stages according to the degree of visual condition, or may be divided into two stages: regions with a relatively good visual condition and regions with a relatively bad visual condition.

The image processing used to generate the simulation image (processing to hide areas with poor visual quality) may be, for example, inpainting (repair processing), interpolation processing, blurring processing, or filling in.

For example, in the inpainting and interpolation processes, a portion of the input image where the visual condition is poor is treated as a degraded area, and information about the degraded area is interpolated based on information about the surrounding area. In this case, the degraded area may be formed by pixels that do not have information about the input image. However, the degraded area does not necessarily have to be formed only by pixels that do not have information about the input image, and some pixels may still have information about the input image. The number of pixels that still have information about the input image may be set according to the visual condition, and may be reduced the worse the visual condition. For example, by performing dithering on the degraded area, pixels that still have information about the input image can be provided within the degraded area. In this case, the inpainting or interpolation process is performed on the pixels that do not have information about the input image.

Various algorithms are known for the inpainting and the interpolation process, and any of the algorithms can be applied as appropriate. For example, the inpainting algorithm may be, for example, an algorithm based on the Fast Marching Method (see Non-Patent Document 2), an algorithm using partial differential equations (e.g., Navier-Stokes equations) (see Non-Patent Document 3), or an algorithm using machine learning (see Non-Patent Document 4). In addition, any of various interpolation methods such as polynomial interpolation, spline interpolation, linear interpolation, and cubic interpolation may be used for the interpolation process.
Telea, Alexandru. “An image inpainting technique based on the fast marching method.” Journal of graphics tools 9.1 (2004): 23-34. Bertalmio, Marcelo, Andrea L. Bertozzi, and Guillermo Sapiro. “Navier-stokes, fluid dynamics, and image and video inpainting.” In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I-355. IEEE, 2001. Liu et al. “Image Inpainting for Irregular Holes Using Partial Convolutions.” arXiv: 1804.07723

In the blurring process, areas with poor visual conditions may be strongly blurred and areas with good visual conditions may be weakly blurred. An average filter, a Gaussian filter, or a median filter may be used for the blurring process. In addition, in the case of filling, the degraded areas (more specifically, each pixel therein that does not have information from the input image) may be filled with a predetermined color (e.g., gray). Alternatively, the entire image or a part of it may be filled with an average color. Furthermore, each area may be filled with a different color depending on the visual condition. For example, areas with relatively good visual conditions may be filled with a light color, and areas with relatively poor visual conditions may be filled with a dark color.

It is preferable that the angle of view of the input image is approximately the same in shape and size as the field of view corresponding to the estimated information. As shown in FIG. 3, when the field of view corresponding to the estimated information is circular and the input image is rectangular, there may be areas in the input image for which there is no corresponding estimated information. In this case, the areas for which there is no corresponding estimated information may be subjected to the image processing of either the inpainting or filling processing described above, as with the degraded areas. Alternatively, extrapolation processing may be performed.

<Output/presentation step>
Next, the PC 20 outputs a simulation image corresponding to the estimated information for presentation to the patient's eye. In the first embodiment, the simulation image is output to the patient terminal 30. Accordingly, the patient terminal 30 presents the simulation image to the patient's eye. As a result, the patient can intuitively grasp the future visual condition through the simulation image. As a result, the patient can be effectively motivated to undergo treatment for the visual field disorder. In other words, it is expected that the patient's adherence will improve.

<Simulation images are output and presented separately for each patient's left and right eyes>
In the first embodiment, in the image processing step, simulation images for the left and right patient's eyes are obtained. The PC 20 may output the simulation images for the left and right patient's eyes to be individually presented to the left and right patient's eyes. That is, the PC 20 may output the simulation images so that the simulation image for the left eye is presented to the left eye, and the simulation image for the right eye is presented to the right eye. Furthermore, together with the simulation images, information for instructing the presentation mode in the terminal device 30 may be output from the PC 20. As an example, in the first embodiment, information for instructing the display positions on the screen 31 of the simulation image for the left eye and the simulation image for the right eye may be output from the PC 20. The terminal device 30 may display the simulation image for the left eye on the left half of the screen 31 and the simulation image for the right eye on the right half of the screen 31 based on the instruction information. The simulation image for the left eye and the simulation image for the right eye may be presented simultaneously, or one at a time. One of the reasons why patients are less aware of symptoms of visual field disorders is that, by viewing with both eyes, the area of visual field missing in one eye is complemented by the other eye. As described above, by simultaneously presenting a simulation image for the left eye and a simulation image for the right eye, the patient can experience how the image looks in a state of binocular vision that takes complementation into consideration. In addition, by presenting a simulation image for each of the patient's left and right eyes one by one, the future vision of each eye can be intuitively and satisfactorily shown. When presenting one by one, the patient terminal 30 may display an indicator (e.g., a symbol such as "L" or "R") that identifies which of the patient's eyes the simulation image is for, simultaneously with the simulation image.

However, when presenting a simulation image for each of the patient's left and right eyes, it is not necessary to present the simulation image to only one eye, and the simulation image corresponding to one eye may be presented to both eyes. In other words, the same simulation image may be presented to both eyes. This allows the patient to clearly observe the future vision in each eye with binocular vision.

<Output and display multiple simulation images corresponding to multiple time points>
In the image processing step, when multiple simulation images corresponding to multiple future time points of the patient's eye are obtained, the PC 20 may output the multiple simulation images to the patient terminal 30. For example, the multiple simulation images corresponding to multiple future time points may be presented to the patient's eye in chronological order by the patient terminal 30. The simulation images may be presented while being switched in chronological order so that the appearance shown by the simulation images transitions from the past or present to the future. However, this is not necessarily limited to this, and multiple simulation images corresponding to multiple time points may be presented to the patient's eye simultaneously (in other words, in parallel). In this case, the patient can view and compare the appearance at multiple time points.

<Generate, output (and present) simulation images for each obstacle progression speed>
The PC 20 may have a computer acquire estimated information indicating the visual state at a certain point in the future for each speed of progression of the obstacle, and in this case, in a further image processing step, a simulation image indicating how the vision will look at a certain point in the future for each speed of progression of the obstacle may be generated.

For example, the change over time in the degree of progression of the disorder is shown by the graph in FIG. 4. In the graph in FIG. 4, the vertical axis indicates the MD (mean deviation) value [dB], and the horizontal axis indicates time. The MD value indicates the average loss of sensitivity in the visual field, and is a type of index obtained by visual field testing that indicates the degree of visual field impairment. In FIG. 4, the straight line (or curve) that is fitted to the MD value at each time point is called the MD slope. The progression rate of the disorder can be described by the slope of the MD slope [dB/year]. For example, the MD value at a past test point can be used to calculate the MD slope when the progression rate does not change between the past and future. The MD value at a certain age can be estimated based on the MD slope.

At this time, the rate at which the disorder progresses differs between cases where the disorder is properly treated and cases where the disorder is not properly treated. The rate of progression is slower when the disorder is properly treated, and is faster when the disorder is not properly treated.

In the first embodiment, a second MD slope different from the above MD slope (for convenience, referred to as the first MD slope) is assumed, and the visual state of the patient's eye at each future time point when the disorder progresses at a different speed from before is specified by the second MD slope. Here, estimated information indicating the visual state at a certain time point in the first MD slope can be obtained based on the above estimation process. Also, by assuming that the MD value and the visual state data have a one-to-one relationship, the visual state at a certain time point in the second MD slope is considered to be the same as the visual state at another time point in the first MD slope. Therefore, based on the correspondence between the first MD slope and the second MD slope by the MD value, the time point at which the visual state is obtained in the above estimation process is corrected, and estimated information indicating the visual state at any time point in the second MD slope can be obtained. Also, based on the obtained estimated information, a simulation image at any time point in the second MD slope can be obtained.

In FIG. 4, the second MD slope indicates a worse prognosis than the first MD slope. The difference in slope between the second MD slope and the first MD slope may be a statistical value based on the MD slopes of multiple patient eyes in the past. This value may be, for example, a default value or a value based on machine learning.

In the first embodiment, as described above, simulation images showing the vision at a certain point in the future are generated for each progression speed of the disorder, and each simulation image is presented to the patient's eye. Since the two simulation images with different progression speeds correspond to a case where treatment is performed appropriately and a case where treatment is not performed, by presenting each simulation image, the patient can easily compare the difference in future vision due to the difference in the progression speed of the disorder. As a result, the patient can be more effectively motivated to undergo treatment.

Note that multiple simulation images with different rates of progression of the disorder may be presented to the patient's eye simultaneously, or may be presented to the patient's eye in an alternating manner. This allows the patient to easily compare the difference in future vision due to the difference in the rate of progression of the disorder. When multiple simulation images with different rates of progression of the disorder are presented to the patient's eye in an alternating manner, the presentation order of the simulation image assuming a slower rate of progression of the disorder and the simulation image assuming an even slower rate of progression of the disorder may be predetermined. The presentation order may also be changeable in response to operational input from the examiner.

When presenting multiple simulation images with different rates of progression of the disorder, the patient may also be presented with information indicating whether or not they will become blind before the end of their lifespan.

The PC 20 may also receive information indicating the degree of disability via a user interface, generate and output a simulation image based on that degree of disability, and determine and output the period until that degree of disability is reached. The information indicating the degree of disability may be, for example, an MD value, or information on early, middle, late, etc. For example, when a certain MD value is received, the PC 20 may obtain information indicating how many years it will take to reach that degree of disability, based on, for example, the MD slope described above, and output this to the display device, along with a simulation image showing how the vision will look at that MD value. The information indicating the period until the input degree of disability is reached may be obtained in multiple quantities, for example, for each progression speed of the disability.

<Displaying a simulation image at a desired time point>
The above-mentioned MD slope can be used even when the PC 20 receives information on the "time point" showing the appearance by the simulation image via a user interface and generates a simulation image at the time point. For example, a simulation image showing the appearance at the time point may be generated based on the MD value corresponding to the input "time point" in the above-mentioned MD slope. This allows the examiner or patient to check the appearance at a desired time point.

The PC 20 may also generate and further output a simulation image at the input "time point" for each progression speed of the disorder (e.g., for each MD slope) based on the correspondence over time (e.g., age) between the first MD slope and the second MD slope, which have different progression speeds of the disorder.

<Comparison with normal vision>
The PC 20 may output the simulation image and the image showing how a normal person sees to be presented to the patient's eye simultaneously or by switching between them alternately. The image showing how a normal person sees may be, for example, an input image. By presenting the simulation image and the image showing how a normal person sees, the patient can better grasp how he or she will see in the future.

Although the above description is based on an embodiment, the present disclosure is not necessarily limited to the above embodiment and can be modified and implemented as appropriate.

<About the entity that runs the visual simulation program>
For example, in the first embodiment, a case has been described in which the visual simulation program is executed in the PC 20. However, this is not necessarily limited to this, and the visual simulation program may be executed in a computer other than the PC 20. For example, the visual simulation program may be executed in either the inspection device 10 or the patient terminal 30, or may be executed in a server computer (not shown).

In the first embodiment, the device for testing the visual function of the patient's eye, the device for obtaining a simulation image based on estimated information, and the device for presenting the simulation image to the patient's eye were separate. However, this is not necessarily limited to this, and any two or all of the above may be integrated. For example, JP 2017-217016 A discloses a head-mounted perimeter. Such a device may further obtain a simulation image based on estimated information and further present the simulation image to the patient's eye.

<Estimating future visual status based on degree classification>
For example, in the first embodiment, the simulation image is described as being generated by image processing performed by the patient terminal 20 based on the estimated information. However, the simulation image does not necessarily have to be obtained by image processing. For example, a plurality of simulation images may be prepared in advance according to the classification of the degree of the disability.

For example, known examples of glaucoma severity classification include the Kosaki classification, the Aulhorn classification Greve modification, and the Humphery perimeter visual field defect severity classification. For example, multiple simulation images may be prepared in advance according to any of these classifications.

<About the output destination of the simulation image in the output/presentation step>
For example, in the first embodiment, a case has been described in which the simulation image is output from the PC 20 to the patient terminal 30 in the output/presentation step. However, this is not necessarily limited to this, and the simulation image may be output to a memory accessible from the PC 20 (e.g., the non-transient memory 22 and the memory of a server computer, etc.). In addition, when the patient terminal 30 itself obtains the simulation image based on the estimated information, the output of the simulation image in the output/presentation step may be realized by a presentation output to the patient's eye via a monitor or a projector.

<Tracking using head-mounted displays>
The terminal device 30 may detect the gaze direction of the patient's eye and control the display position of the simulation image on the screen according to the gaze direction (tracking). This allows the patient to appropriately observe the future visual condition of the patient's eye even if the gaze direction of the patient's eye changes.

<Another embodiment of the display device (presentation device)>
Also, for example, in the above embodiment, the display device for presenting the simulation image to the patient has been described as a head-mounted display (patient terminal 30). However, this is not necessarily limited to this. The display device may be, for example, a stationary display, a projector that projects an image onto a screen, or another display device. When a stationary display or projector is used as the display device, the estimated visual condition can be viewed not only by the patient but also by the patient's family and the examiner (for example, a doctor), which is useful for informed consent.

In the above embodiment, a head-mounted display (patient terminal 30) was used as a display device, making it possible to present images independently to the left and right eyes of the patient. However, for example, when a stationary display or projector as described above is used as a display device, the same image is presented to both of the patient's eyes. In this case, a simulation image showing the appearance in a binocular vision state at a certain point in time may be displayed on the display device. The simulation image showing the appearance in a binocular vision state on a single sheet may be, for example, a logical sum of two simulation images for each of the left and right eyes. The simulation image showing the appearance in a binocular vision state on a single sheet may be displayed on various display devices, not limited to when the display device is a stationary display or projector.

<Application to estimation results of visual condition related to the anterior eye>
In the above embodiment, a case has been described in which the retinal function at a certain time is estimated as the visual function of the patient's eye, and a simulation image is generated and presented based on the estimated retinal function. As the visual function of the patient's eye, a function related to the anterior segment of the patient's eye at a certain time may be estimated, and a simulation image may be generated and presented based on the estimated function of the anterior segment. In addition, the functions of both the anterior segment and the retina may be estimated, and a simulation image may be generated and presented taking into account both estimated functions. As the function of the anterior segment, for example, the refractive function (ocular refractive power) may be estimated, the axial length value may be estimated, the accommodation function (accommodation power) may be estimated, or the degree of opacity in the optical medium may be estimated. These various functions may be appropriately measured by various ophthalmic measuring devices.

For example, when the refractive function is estimated, a PSF (point spread function) according to the estimated refractive power may be acquired, and a simulation image showing the appearance at the estimated refractive power may be generated by image processing (e.g., convolution integration) of the PSF and the input image. A simulation image may also be generated by image processing (convolution integration) of the optical transfer function (OTF) obtained by further Fourier transforming the point spread function (PSF) and the input image. The refractive power is estimated, for example, based on multiple past examinations. For example, it may be estimated based on a fitting straight line (or curve) of past examination points, or may be a value based on machine learning.

In estimating the visual function of the patient's eye, other measurement results of the patient's eye may be used. Furthermore, the visual function of the patient's eye may be estimated using the results of any examination device that images the body other than the eye, including the brain, such as MRI, CT, PET, or ultrasound.

<Supplementary Note: Embodiments Contained in This Disclosure>
Furthermore, an embodiment of the present disclosure may be the following first visual simulation method and a visual simulation program for causing a computer to execute the visual simulation method.

The first visual simulation method includes an acquisition step of acquiring different simulation images between the left and right eyes of the patient's eye, which are simulation images showing how the patient's eye (examined eye) sees in a visual state different from the current visual state of the patient's eye, and a presentation step of simultaneously presenting a simulation image for the left eye to the left eye and a simulation image for the right eye to the right eye via a display device capable of presenting images independently to the left and right eyes of the patient's eye. According to the first visual simulation method, the appearance when the visual state changes can be individually imagined for the left and right eyes, and the patient (examinee) can experience the appearance with binocular vision at that time. Therefore, the appearance when the visual state changes can be experienced in a form that is closer to the actual appearance.

10 Eye examination device 20 PC
30 Patient terminal E Patient eye

Claims (2)

An estimated information acquisition step in which a computer acquires estimated information indicating a visual condition of a patient's eye at a certain point in time;
an output step in which the computer outputs a simulation image showing a vision in the patient's eye at the certain time point, the simulation image corresponding to the estimated information;
In the estimated information acquisition step, the estimated information of each of the patient's left and right eyes is acquired,
In the output step, the simulation images corresponding to the estimated information for each of the left and right patient's eyes are output to be presented to the left and right patient's eyes individually,
Furthermore,
In the estimated information acquisition step, first estimated information in a case where a progression speed of the obstacle is a first speed and second estimated information in a case where the progression speed of the obstacle is a second speed different from the first speed are acquired as the estimated information indicating a visual state at a certain time point in the future;
The computer generates a first simulation image based on the first estimated information and a second simulation image based on the second estimated information as the simulation images in which the progression speeds of an obstacle are different for the same field of view. A visual simulation program that causes the computer to execute the visual simulation method described in claim 1.