Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System

Study Overview
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Brief Summary
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Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions. In this study, we developed a deep learning system DeepMyopia, based on fundus images with the following objectives: 1) to predict myopia onset and progression; 2) To detect myopic macular degeneration for AI-assisted diagnosis; 3) To predict the development of myopic macular degeneration; 4) evaluate its cost-effectiveness.

Detailed Description
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Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Furthermore, as myopia progresses it increases the risk of ocular complications such as myopic macular degeneration, leading to irreversible visual impairment or even blindness. According to the World Health Organization , more than 1 billion people worldwide are living with vision impairment caused by myopia, hyperopia, and other problems due to late detection. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions. In this study, we developed a deep learning system DeepMyopia, based on fundus images with the following objectives: 1) to predict myopia onset and progression; 2) To detect myopic macular degeneration for AI-assisted diagnosis; 3) To predict the development of myopic macular degeneration; 4) evaluate its cost-effectiveness.

Official Title
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Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System

Conditions
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Myopia, Myopic Macular Degeneration

Intervention / Treatment
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* Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subjects with fundus images in the Shanghai Child and Adolescent Large-scale Eye Study (SCALE) ; Subjects with fundus images in the Shanghai Time Outside to Reduce Myopia [STORM] trial; Subjects with fundus images in the High Myopia Registration Study [SCALE-HM] Subjects with fundus images in the Shanghai Myopia Screening (SMS) Study; Subjects with fundus images in the Beijing Children Eye Study Subjects with fundus images in the First Affiliated Hospital of Kunming Medical University; Subjects with fundus images at the Ophthalmology Department of the First Affiliated Hospital of Xinjiang Medical University; Subjects with fundus images at the Ophthalmology Department of the Affiliated Hospital of Inner Mongolia Medical University; Subjects with fundus images at Zhongshan Eye Centre, Sun Yat-sen University; Subjects with fundus images in the Hong Kong Children Eye Study; Exclusion Criteria: Participants with poor-quality fundus images

Ages Eligible for Study
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Minimum Age: 4 Years
Maximum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| The training dataset<br>The training dataset was comprised of data from a school-based, prospective cohort (the Shanghai Time Outside to Reduce Myopia [STORM] trial) and data from another population-based, prospective study, the High Myopia Registration Study (SCALE-HM), with annual follow-up. Participants of the two studies were divided into a training set (70%), a tuning set (10%), and an internal test set (20%), which were not duplicated by each other at the participant level. | Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system<br>* This deep learning system is capable of analyzing fundus images for myopia staging, myopic maculopathy detection, cycloplegic refraction estimation and prediction, and risk stratification of myopia and myopic maculopathy onset.<br>|
| The internal validation dataset<br>The internal validation dataset was comprised of data from a school-based, prospective cohort (the Shanghai Time Outside to Reduce Myopia [STORM] trial) and data from another population-based, prospective study, the High Myopia Registration Study (SCALE-HM), with annual follow-up. Participants of the two studies were divided into a training set (70%), a tuning set (10%), and an internal test set (20%), which were not duplicated by each other at the participant level. | Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system<br>* This deep learning system is capable of analyzing fundus images for myopia staging, myopic maculopathy detection, cycloplegic refraction estimation and prediction, and risk stratification of myopia and myopic maculopathy onset.<br>|
| The external validation dataset<br>To test the extrapolation capabilities of the deep learning sysyem, two independent datasets, the Joint Five-site Fundus Test (JFFT) and the Hong Kong Children Eye Study (HKCES), were applied as external test sets. The JFFT study, a multi-site dataset, contains cross-sectional data from Shanghai, Yunnan, Inner Mongolia, Xinjiang and Guangzhou. HKCES, a population-based cohort study of eye conditions in children aged 6-8 years. | Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system<br>* This deep learning system is capable of analyzing fundus images for myopia staging, myopic maculopathy detection, cycloplegic refraction estimation and prediction, and risk stratification of myopia and myopic maculopathy onset.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| myopia staging detection possibility score | output of myopia staging task | immediately after inputting the data | 
| myopic maculopathy detection possibility score | output of myopic maculopathy detection task | immediately after inputting the data | 
| predicted spherical equivalent | output of assessing spherical equivalent task | immediately after inputting the data | 
| predicted future annual spherical equivalent | output of predicting future spherical equivalent task | immediately after inputting the data | 
| risk score of myopia and myopic maculopathy progression | output of the progression of myopia and myopic maculopathy predicion task | immediately after inputting the data |