ArticleFrontiers in medicine2026
Application of a deep learning-based system for eyelid margin signs identification training and testing in dry eye disease education.
Article in Frontiers in medicine, 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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Abstract
Background: Dry eye disease (DED) is a common ocular surface disease, and identifying abnormal eyelid margin signs is essential for clinical diagnosis. However, traditional teaching methods lack effective feedback and often lead to low learning efficiency. This study aimed to develop an interactive teaching platform based on a deep learning model for eyelid margin signs and to evaluate its effectiveness. Methods: This randomized controlled trial included 40 medical students from the Peking University Health Science Center. Participants were randomly assigned to a conventional learning group ( Results: The platform learning group achieved a significantly higher total score than the conventional learning group (56.95 ± 5.95 vs. 53.37 ± 4.32, Conclusion: The deep learning-integrated teaching platform may improve students' overall accuracy in identifying abnormal eyelid margin signs, particularly MGO plugging. This platform may serve as a useful supplement to traditional clinical ophthalmology education and may help improve the efficiency of learning eyelid margin assessment.
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