Application of a deep learning-based system for eyelid margin signs identification training and testing in dry eye disease education.

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 (n = 19) or a platform learning group (n = 21). After baseline training, the platform group practiced using the AI-assisted system with real-time feedback, while the conventional group reviewed video lectures. Two weeks later, all students took a timed final exam evaluating eight core abnormal eyelid margin signs.

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, p = 0.035). Specifically, the platform group showed a significantly higher accuracy rate in identifying meibomian gland orifice (MGO) plugging (77.6% vs. 67.4%, p = 0.021). Subgroup analysis revealed that the platform significantly improved the accuracy of rounding of posterior lid margin for undergraduate students (82.1% vs. 69.1%, p = 0.016), and the accuracy of MGO plugging for graduate students (85.7% vs. 67.5%, p = 0.009).

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.