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EED-Astig: A Multimodal Dataset for Pediatric Astigmatism Severity Prediction
Haihua Liu1, Shengyang Li2,3, Yixuan Lv4
1Department of Ophthalmology Center, Peking University First Hospital, Beijing, 100034, China.
Scientific Data
|May 1, 2026
Summary
A new AI dataset, EED-Astig, uses smartphone images to predict astigmatism in young children. This approach offers a cost-effective solution for early visual impairment screening in pediatric eye health.
Area of Science:
- Ophthalmology
- Computer Science
- Pediatric Healthcare
Background:
- Astigmatism is a common refractive error in preschoolers, leading to preventable visual impairment.
- Current screening methods are often impractical for young children due to cost, equipment, and cooperation requirements.
Purpose of the Study:
- To introduce EED-Astig, a multimodal pediatric dataset for AI-driven astigmatism severity prediction.
- To develop scalable and interpretable AI tools for early detection of pediatric eye conditions, especially in underserved areas.
Main Methods:
- Collected periocular images from 640 children (aged 3-6) using smartphones under standardized conditions.
- Utilized expert-verified annotations and the Segment Anything Model (SAM) for a semi-automated annotation pipeline.
- Derived six structural parameters and incorporated behavioral/demographic metadata for supervised learning.
Main Results:
- Technical validation showed robust performance in keypoint detection and image segmentation.
- The dataset enables the derivation of clinically relevant structural parameters linked to astigmatism.
- The multimodal approach integrates imaging and metadata for comprehensive analysis.
Conclusions:
- The EED-Astig dataset supports the development of AI tools for pediatric astigmatism prediction.
- This AI-based approach offers a promising, accessible solution for early visual screening in children.
- The findings highlight the potential for improving pediatric eye health outcomes, particularly in resource-limited settings.
