Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System
Condition: Myopia; Myopic Macular Degeneration
MeSH terms: Macular Degeneration; Myopia
Study type: Observational

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.
    

Intervention name(s): A deep learning-based myopia and myopic maculopathy detection and prediction system
Intervention type(s): Diagnostic Test
Intervention description(s): 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.
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.