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

Completed

• Conditions: Myopia; Myopic Macular Degeneration
• Interventions: Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system

• Registration Number: NCT05835115
• Lead Sponsor: Shanghai Eye Disease Prevention and Treatment Center

Brief Summary

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

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.

Study Timeline

• Study Start: 2022-04-01
• Status Verified: 2023-04
• Primary Completion: 2023-04-01
• Study Completion: 2023-04-01
• Study First Submitted: 2023-04-18
• Study First Submitted QC: 2023-04-18
• Last Update Submitted: 2023-04-18
• Study First Posted: 2023-04-28
• Last Update Posted: 2023-04-28

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 30526

Inclusion Criteria

1. Subjects with fundus images in the Shanghai Child and Adolescent Large-scale Eye Study (SCALE) ;
2. Subjects with fundus images in the Shanghai Time Outside to Reduce Myopia [STORM] trial;
3. Subjects with fundus images in the High Myopia Registration Study [SCALE-HM]
4. Subjects with fundus images in the Shanghai Myopia Screening (SMS) Study;
5. Subjects with fundus images in the Beijing Children Eye Study
6. Subjects with fundus images in the First Affiliated Hospital of Kunming Medical University;
7. Subjects with fundus images at the Ophthalmology Department of the First Affiliated Hospital of Xinjiang Medical University;
8. Subjects with fundus images at the Ophthalmology Department of the Affiliated Hospital of Inner Mongolia Medical University;
9. Subjects with fundus images at Zhongshan Eye Centre, Sun Yat-sen University;
10. Subjects with fundus images in the Hong Kong Children Eye Study;

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Exclusion Criteria

• Participants with poor-quality fundus images

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
The training dataset	A deep learning-based myopia and myopic maculopathy detection and prediction system	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.
The internal validation dataset	A deep learning-based myopia and myopic maculopathy detection and prediction system	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.
The external validation dataset	A deep learning-based myopia and myopic maculopathy detection and prediction system	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.

Primary Outcome Measures

Name	Time	Method
predicted spherical equivalent	immediately after inputting the data	output of assessing spherical equivalent task
predicted future annual spherical equivalent	immediately after inputting the data	output of predicting future spherical equivalent task
myopia staging detection possibility score	immediately after inputting the data	output of myopia staging task
myopic maculopathy detection possibility score	immediately after inputting the data	output of myopic maculopathy detection task
risk score of myopia and myopic maculopathy progression	immediately after inputting the data	output of the progression of myopia and myopic maculopathy predicion task

Trial Locations

Locations (1)

Shanghai Eye Disease Prevention and Treatment Center; 🇨🇳 Shanghai, Shanghai, China