EP-4736112-A1 - METHODS, DEVICES, AND APPARATUSES FOR TESTING IMAGING QUALITY FOR NYSTAGMUS PATIENTS

Abstract

A method for testing imaging quality for nystagmus patients. The method comprises: obtaining preoperative movement data of an eyeball of a nystagmus patient in real time;adjusting a position of a visual target according to the preoperative movement data such that the adjusted position of the visual target is consistent with a sight direction in the preoperative movement data;evaluating the imaging quality for the nystagmus patient, according to a vision imaging result when the sight direction is consistent with the position of the visual target, to obtain an evaluation result; and predicting a postoperative result of the nystagmus patient according to the evaluation result and a pre-trained treatment evaluation model.

Inventors

• WANG, LEJIN
• WANG, Wensi
• XIAO, ZHEN
• WANG, Tianfang
• WANG, DUO

Assignees

• Chaomu Technology (Beijing) Co., Ltd.

Dates

Publication Date

20260506

Application Date

20241011

Claims (20)

1. A method for testing imaging quality for a nystagmus patient comprising: obtaining preoperative movement data of an eyeball of a nystagmus patient in real time; adjusting a position of a visual target according to the preoperative movement data such that the adjusted position of the visual target is consistent with a sight direction in the preoperative movement data; evaluating the imaging quality for the nystagmus patient, according to a vision imaging result when the sight direction is consistent with the position of the visual target, to obtain an evaluation result; and predicting a postoperative result of the nystagmus patient according to the evaluation result and a pre-trained treatment evaluation model.
2. The method of claim 1, wherein the step of adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to build an eye movement prediction model; predicting movement data of the eyeball at the next moment according to the eye movement prediction model and the movement data of the eyeball at the current moment; and adjusting the position of the visual target according to the sight direction determined by the predicted movement data of the eyeball at the next moment.
3. The method of claim 2, wherein the step of using the historical movement data to build the eye movement prediction model comprises: performing fitting analysis for the historical movement data based on the anatomical and physiological principles to build the eye movement prediction model; or using the historical movement data to train an autoencoder to build the eye movement prediction model.
4. The method of claim 1, wherein the step of adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to train a machine learning model to obtain a nystagmus identification model for identifying a speed and an amplitude of the nystagmus; inputting the movement data as obtained in real time into the nystagmus identification model to obtain the current speed and amplitude of the nystagmus; and adjusting the position of the visual target according to the current speed and amplitude of the nystagmus.
5. The method of any one of claims 1-4, wherein after the step of obtaining the preoperative movement data of the eyeball of the nystagmus patient in real time, the method further comprises performing filtering processing to the preoperative movement data according to an adaptive filtering algorithm.
6. The method of any one of claims 1-5, wherein the step of predicting the postoperative result of the nystagmus patient according to the evaluation result and the pre-trained treatment evaluation model comprises: obtaining a historical evaluation result of the eyeball of the nystagmus patient and a corresponding treatment result; using the historical evaluation result and the corresponding treatment result to train the machine learning model to obtain the treatment evaluation model; and inputting the evaluation result into the treatment evaluation model to determine the postoperative result of the nystagmus patient.
7. The method of any one of claims 1-6, wherein the evaluation result comprises visual clarity, contrast, and stability.
8. A device for testing imaging quality for a nystagmus patient, comprising: a data obtaining module, for obtaining preoperative movement data of an eyeball of a nystagmus patient in real time; an adjusting module, for adjusting a position of a visual target according to the preoperative movement data such that the adjusted position of the visual target is consistent with a sight direction in the preoperative movement data; an evaluation module, for evaluating the imaging quality for the nystagmus patient, according to a vision imaging result when the sight direction is consistent with the position of the visual target, to obtain an evaluation result; and a prediction module, for predicting a treatment result of the nystagmus patient according to the evaluation result and a pre-trained treatment evaluation model.
9. The device of claim 8, wherein adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to build an eye movement prediction model; predicting movement data of the eyeball at the next moment according to the eye movement prediction model and the movement data of the eyeball at the current moment; and adjusting the position of the visual target according to the sight direction determined by the movement data of the eyeball at the next moment.
10. The device of claim 9, wherein using the historical movement data to build the eye movement prediction model comprises: performing fitting analysis for the historical movement data based on the anatomical and physiological principles to build the eye movement prediction model; or using the historical movement data to train an autoencoder to build the eye movement prediction model.
11. The device of claim 8, wherein adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to train a machine learning model to obtain a nystagmus identification model for identifying a speed and an amplitude of the nystagmus; inputting the movement data as obtained in real time into the nystagmus identification model to obtain the current speed and amplitude of the nystagmus; and adjusting the position of the visual target according to the current speed and amplitude of the nystagmus.
12. The device of any one of claims 8-11, further comprising a filtration module, for performing filtering processing to the preoperative movement data according to an adaptive filtering algorithm.
13. The device of any one of claims 8-12, wherein predicting the postoperative result of the nystagmus patient according to the evaluation result and the pre-trained treatment evaluation model comprises: obtaining a historical evaluation result of the eyeball of the nystagmus patient and a corresponding treatment result; using the historical evaluation result and the corresponding treatment result to train the machine learning model to obtain the treatment evaluation model; and inputting the evaluation result into the treatment evaluation model to determine the postoperative result of the nystagmus patient.
14. The device of any one of claims 8-13, wherein the evaluation result comprises visual clarity, contrast, and stability.
15. An apparatus for testing imaging quality for a nystagmus patient comprising: a display device, for displaying a visual target; an eye movement tracking device, for tracking movement of an eyeball of a nystagmus patient in real time to obtain preoperative movement data; and a testing device, for adjusting a position of a visual target according to the preoperative movement data such that the adjusted position of the visual target is consistent with a sight direction in the preoperative movement data; evaluating the imaging quality for the nystagmus patient, according to a vision imaging result when the sight direction is consistent with the position of the visual target, to obtain an evaluation result; and predicting a treatment result of the nystagmus patient according to the evaluation result and a pre-trained treatment evaluation model.
16. The apparatus of claim 15, wherein adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to build an eye movement prediction model; predicting movement data of the eyeball at the next moment according to the eye movement prediction model and the movement data of the eyeball at the current moment; and adjusting the position of the visual target according to the sight direction determined by the movement data of the eyeball at the next moment.
17. The apparatus of claim 15, wherein adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to train a machine learning model to obtain a nystagmus identification model for identifying a speed and an amplitude of the nystagmus; inputting the movement data as obtained in real time into the nystagmus identification model to obtain the current speed and amplitude of the nystagmus; and adjusting the position of the visual target according to the current speed and amplitude of the nystagmus.
18. The apparatus of claim 16, wherein using the historical movement data to build the eye movement prediction model comprises: performing fitting analysis for the historical movement data based on the anatomical and physiological principles to build the eye movement prediction model; or using the historical movement data to train an autoencoder to build the eye movement prediction model.
19. The apparatus of any one of claims 15-18, wherein predicting the postoperative result of the nystagmus patient according to the evaluation result and the pre-trained treatment evaluation model comprises: obtaining a historical evaluation result of the eyeball of the nystagmus patient and a corresponding treatment result; using the historical evaluation result and the corresponding treatment result to train the machine learning model to obtain the treatment evaluation model; and inputting the evaluation result into the treatment evaluation model to determine the postoperative result of the nystagmus patient.
20. [Corrected under Rule 26, 24.10.2024] The apparatus of any one of claims 15-19, wherein the eye movement tracking device is also configured to perform filtering processing to the preoperative movement data according to an adaptive filtering algorithm.

Description

METHODS, DEVICES, AND APPARATUSES FOR TESTING IMAGING QUALITY FOR NYSTAGMUS PATIENTS CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Chinese Patent Application No. 202311317634.5, filed October 12, 2023, granted as CN 117058148 B on February 2, 2024, the content of which is incorporated by reference in its entirety herein. 1. TECHNICAL FIELD The present disclosure relates to medical imaging testing, and specifically to methods, devices, and apparatuses for testing imaging quality for nystagmus patients. 2. BACKGROUND Nystagmus patients are different from general myopia patients who can achieve improved vision by correction through glasses. This is because nystagmus cannot be corrected by glasses, and is generally treated by operations and/or surgeries. In contrast to those conditions that may be conveniently treated with glasses, it is difficult to predict the postoperative vision level of nystagmus patients after operations. Furthermore, in addition to nystagmus, there may be other eye diseases affecting the vision or other factors causing hypoplasia. However, the current testing methods are mainly for a stable visual target, but cannot accurately evaluate the imaging quality. Though the eye movement tracking device can capture the data of nystagmus, it is still necessary for the surgeons to participate the determination and evaluation of the eye symptoms of the patients. Currently, there is no effective preoperative evaluation tool, and thus surgeons cannot predict the postoperative visual correction effects of the patients quickly and accurately. 3. SUMMARY The present disclosure provides apparatuses and methods for testing imaging quality of eyeballs of nystagmus patients, which can solve the problems in the art: that the postoperative imaging quality of the eyeballs of the nystagmus patients cannot be accurately evaluated before operations and/or surgeries. In a first aspect of the present disclosure, a method for testing imaging quality for a nystagmus patient is provided, wherein the method comprises: obtaining preoperative movement  data of an eyeball of a nystagmus patient in real time; adjusting a position of a visual target according to the preoperative movement data such that the adjusted position of the visual target is consistent with a sight direction in the preoperative movement data; evaluating the imaging quality for the nystagmus patient, according to a vision imaging result when the sight direction is consistent with the position of the visual target, to obtain an evaluation result; and predicting a postoperative result of the nystagmus patient according to the evaluation result and a pre-trained treatment evaluation model. In certain embodiments of the method disclosed herein, by obtaining preoperative movement data of the nystagmus patient in real time, it is possible to record the movement information of the eyeball of the patient accurately, providing an accurate data basis for the subsequent analysis and adjustment. By adjusting a position of a visual target according to the preoperative movement data, it is possible to ensure that the sight line of the eyeball of the patient is kept consistent with the visual target, better sensing the movement of the eyeball of the patient. By a manner of predicting a postoperative result of the nystagmus patient based on the evaluation result and a pre-trained treatment evaluation model, the quality of the eye of the nystagmus patient after operation is predicted, thus providing accurate evaluation for the postoperative effect and helping the surgeon and the patient to understand the effect and progress of the operation treatment for the nystagmus. In certain embodiments of the method disclosed herein, the step of adjusting the position of the visual target according to the preoperative movement data comprises: obtaining preoperative historical movement data of the eyeball of the nystagmus patient; using the historical movement data to build an eye movement prediction model; predicting movement data of the eyeball at the next moment according to the eye movement prediction model and the movement data of the eyeball at the current moment; and adjusting the position of the visual target according to the sight direction determined by the predicted movement data of the eyeball at the next moment. By predicting the movement data of the eyeball at the next moment in real time and accordingly adjusting the visual target, it is possible to provide fast feedback and adjustment during practical imaging, thus ensuring the imaging quality in real time, and in turn ensuring the accuracy of the postoperative result of the nystagmus patient as predicted later according to the evaluation result and a pre-trained treatment evaluation model. In certain embodiments of the method disclosed herein, the step of using the historical movement data to build the eye movement prediction model comprises: performing fitting analysis for t