Clinical trial NCT05835115: Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System
Official title: Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Myopia
Myopic Macular Degeneration
Lead sponsor: Shanghai Eye Disease Prevention and Treatment Center
Sponsor class: OTHER
Collaborators: name: Shanghai Jiao Tong University School of Medicine
name: Beijing Friendship Hospital
name: Peking Union Medical College Hospital
name: Zhongshan Ophthalmic Center, Sun Yat-sen University
name: First Affiliated Hospital of Kunming Medical University
name: The Affiliated Hospital of Inner Mongolia Medical University
name: First Affiliated Hospital of Xinjiang Medical University
name: Chinese University of Hong Kong
First posted: 2023-04-28
Enrollment: 30526
Sex: ALL
Minimum age (years): 4
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
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.

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.

Eligibility criteria: 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;

Exclusion Criteria:

* Participants with poor-quality fundus images

Primary outcomes:
measure: myopia staging detection possibility score
timeFrame: immediately after inputting the data
measure: myopic maculopathy detection possibility score
timeFrame: immediately after inputting the data
measure: predicted spherical equivalent
timeFrame: immediately after inputting the data
measure: predicted future annual spherical equivalent
timeFrame: immediately after inputting the data
measure: risk score of myopia and myopic maculopathy progression
timeFrame: immediately after inputting the data
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Shanghai Eye Disease Prevention and Treatment Center
state: Shanghai
Study officials:
References: