ArticleTrials2026
Development of artificially intelligent tool for analysis and prediction of myopia progression among school-going children.
Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
Funding
Abstract
backgroundMyopia is a growing public health concern among school-going children, with increased screen time and reduced outdoor activities contributing to its early onset and rapid progression. In India, the absence of a standardized, population-based dataset reflecting socio-economic and environmental diversity limits the development of accurate prediction tools. This study aims to design and validate an artificial intelligence (AI)-based model to analyze and predict myopia progression among children aged 6 to 18 years in urban and rural areas.
methodsThis is a prospective, community-based study conducted in two phases across five districts of Bhopal division, India. Phase one involves a cross-sectional survey of approximately 5,000 school children to collect demographic and ocular health data, including visual acuity, refractive error, axial length, corneal parameters, and lifestyle factors. In phase two, a longitudinal cohort comprising 10% of the phase one participants (balanced for existing and non-existing myopia cases) will be followed every 6 months over 2 years. Machine learning algorithms, including linear regression, support vector machines, XGBoost, and deep learning models such as convolutional neural networks, will be trained on this dataset. An AI-based mobile application will be developed to enable field-level prediction and screening. DISCUSSION: This study will generate the first large-scale Indian dataset on myopia progression among children, incorporating diverse socio-economic backgrounds. The validated AI model and mobile application will support early identification of at-risk children, optimize resource allocation, and inform national screening strategies. By integrating real-time data analytics with field-l.
trial registrationCTRI/2025/07/091243. Registered on 21/07/2025.
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