Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System
NCT ID: NCT05835115
Last Updated: 2023-04-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
30526 participants
OBSERVATIONAL
2022-04-01
2023-04-01
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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OTHER
RETROSPECTIVE
Study Groups
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The training dataset
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.
A deep learning-based myopia and myopic maculopathy detection and prediction system
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.
The internal validation dataset
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.
A deep learning-based myopia and myopic maculopathy detection and prediction system
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.
The external validation dataset
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.
A deep learning-based myopia and myopic maculopathy detection and prediction system
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.
Interventions
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A deep learning-based myopia and myopic maculopathy detection and prediction system
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.
Eligibility Criteria
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Inclusion Criteria
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;
Exclusion Criteria
4 Years
18 Years
ALL
No
Sponsors
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Shanghai Jiao Tong University School of Medicine
OTHER
Beijing Friendship Hospital
OTHER
Peking Union Medical College Hospital
OTHER
Zhongshan Ophthalmic Center, Sun Yat-sen University
OTHER
First Affiliated Hospital of Kunming Medical University
OTHER
The Affiliated Hospital of Inner Mongolia Medical University
OTHER
First Affiliated Hospital of Xinjiang Medical University
OTHER
Chinese University of Hong Kong
OTHER
Shanghai Eye Disease Prevention and Treatment Center
OTHER
Responsible Party
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Locations
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Shanghai Eye Disease Prevention and Treatment Center
Shanghai, Shanghai Municipality, China
Countries
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Other Identifiers
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2022SQ023
Identifier Type: -
Identifier Source: org_study_id