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

NCT05835115 | Completed

• 1 other identifier: 2022SQ023
• Study Type: observational
• Target: 30,526
• Locations: 1 country
• Sites: 1

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.

Conditions

Myopia; Myopic Macular Degeneration

Outcome Measures

Primary Outcomes (5)

• 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

Study Arms (3)

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.

Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system

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.

Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system

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.

Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system

Interventions

A deep learning-based myopia and myopic maculopathy detection and prediction system DIAGNOSTIC_TEST

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; The internal validation dataset; The training dataset

Eligibility Criteria

• Age: 4 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Probability Sample

Study Population

The SCALE, a prospective, school-based study, includes all children aged 4 to 14 years in Shanghai. The SCALE-HM, a population-based, prospective, examiner-masked study, includes children and adolescents aged between 4 and 18 years with high myopia. The STORM trial, a school-based, prospective, examiner-masked, cluster-randomized trial, includes children aged 6 to 9 years. The SMS study is a school-based cross-sectional survey from Shanghai, including kindergarten and primary school students in Year 1 and 2. The Beijing Children Eye study included children who came to the outpatient clinic of Beijing Friendship Hospital. The JFFT study contains cross-sectional data from Shanghai, Yunnan, Inner Mongolia, Xinjiang and Guangzhou. The Hong Kong Children Eye Study is a population-based cohort study of eye conditions in children aged 6-8 years.

You may qualify if:

• 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;

You may not qualify if:

• Participants with poor-quality fundus images

Sponsors & Collaborators

• Shanghai Eye Disease Prevention and Treatment Center: lead
• Shanghai Jiao Tong University School of Medicine: collaborator
• Beijing Friendship Hospital: collaborator
• Peking Union Medical College Hospital: collaborator
• Zhongshan Ophthalmic Center, Sun Yat-sen University: collaborator
• First Affiliated Hospital of Kunming Medical University: collaborator
• The Affiliated Hospital of Inner Mongolia Medical University: collaborator
• First Affiliated Hospital of Xinjiang Medical University: collaborator
• Chinese University of Hong Kong: collaborator

Study Sites (1)

Shanghai Eye Disease Prevention and Treatment Center

Shanghai, Shanghai Municipality, 200041, China

Location

MeSH Terms

Conditions

Myopia

Condition Hierarchy (Ancestors)

Refractive Errors; Eye Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 18, 2023
• First Posted: April 28, 2023
• Study Start: April 1, 2022
• Primary Completion: April 1, 2023
• Study Completion: April 1, 2023
• Last Updated: April 28, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)