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<p>Astigmatism is a prevalent refractive error in preschool children and a leading cause of preventable early visual impairment. Conventional screening methods are often unsuitable for young children due to high costs, specialized equipment, and the need for active cooperation. To address these challenges, we present EED-Astig, a multimodal pediatric dataset for artificial intelligence based astigmatism severity prediction. The dataset comprises periocular images from 640 children aged 3–6 years, acquired with smartphones under standardized conditions, with expert-verified annotations of corneal masks and anatomical landmarks. From these, we derive six clinically relevant structural parameters, including corneal exposure ratio and eyelash orientation, that are physiologically linked to astigmatism. In addition, behavioral and demographic metadata (e.g., screen time, birth history) provide complementary predictors for supervised learning. A semi-automated annotation pipeline based on the Segment Anything Model (SAM) ensures labeling consistency and quality. Technical validation demonstrates robust performance in keypoint detection and image segmentation, supporting the development of interpretable and scalable AI tools for pediatric eye health, particularly in low-resource settings.</p>

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EED-Astig: A Multimodal Dataset for Pediatric Astigmatism Severity Prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月1日

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  • Haihua Liu,
  • Shengyang Li,
  • Yixuan Lv,
  • Rongjun Liu,
  • Xinlin Hou,
  • Furui Chen,
  • Yuxuan Liu,
  • Jianing You,
  • Han Wang,
  • Silei Liu

摘要

Astigmatism is a prevalent refractive error in preschool children and a leading cause of preventable early visual impairment. Conventional screening methods are often unsuitable for young children due to high costs, specialized equipment, and the need for active cooperation. To address these challenges, we present EED-Astig, a multimodal pediatric dataset for artificial intelligence based astigmatism severity prediction. The dataset comprises periocular images from 640 children aged 3–6 years, acquired with smartphones under standardized conditions, with expert-verified annotations of corneal masks and anatomical landmarks. From these, we derive six clinically relevant structural parameters, including corneal exposure ratio and eyelash orientation, that are physiologically linked to astigmatism. In addition, behavioral and demographic metadata (e.g., screen time, birth history) provide complementary predictors for supervised learning. A semi-automated annotation pipeline based on the Segment Anything Model (SAM) ensures labeling consistency and quality. Technical validation demonstrates robust performance in keypoint detection and image segmentation, supporting the development of interpretable and scalable AI tools for pediatric eye health, particularly in low-resource settings.