School-level prediction and management of myopia in children and adolescents
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View abstract on PubMed
Summary
This summary is machine-generated.A new AI model accurately predicts myopia onset and progression in children using school data. This tool enhances myopia management from early detection to long-term progression tracking.
Area Of Science
- Ophthalmology
- Artificial Intelligence
- Public Health
Background
- Existing Artificial Intelligence (AI) models for myopia prediction primarily use hospital data.
- There is a lack of school-level AI models for predicting myopia onset in non-myopic children.
- Comprehensive tools are needed for full myopia management, covering both onset and progression.
Purpose Of The Study
- To develop and validate school-level AI models for predicting myopia occurrence and progression in children.
- To create a user-friendly tool for myopia management in school settings.
Main Methods
- Machine learning models were trained and validated on data from 870 centers across seven Chinese cities (N=1,105,271 children aged 4-18).
- Models were externally tested on data from six cities over a two-year follow-up period.
- Data analysis included prediction of myopia occurrence and spherical equivalent refraction change, with age segmentation.
Main Results
- The study established optimal AI models for predicting myopia occurrence (AUC 0.962) and progression (AUC 0.923) in the external test set.
- Model performance improved with age segmentation (4-11 years and 11-18 years).
- User-friendly software was developed for school-based application.
Conclusions
- The developed AI algorithm demonstrates excellent performance in predicting myopia occurrence and progression in school settings.
- The findings indicate significant potential for this tool in school-level myopia management.
- The study provides a novel approach to proactive myopia control in pediatric populations.
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