Optimizing dry eye disease diagnosis: integrating deep learning insights from OSDI questionnaires and ocular blinking videos

This study introduces a novel and comprehensive diagnostic approach for Dry Eye Disease (DED) by combining a dedicated Ocular Surface Disease Index (OSDI) questionnaire and a measurement system tailored for Chinese citizens with the implementation of the YOLOv8 deep learning model. The research involves the analysis of 52 real-world ophthalmic videos to detect eye blinking conditions, with the model trained to identify abnormal blinking patterns through feature extraction such as blink frequency, duration, and irregularities. Performance metrics, including mean Average Precision (mAP), specificity, recall, f1-score, and Frame Per Second (FPS), are measured on a PC (CPU, Core i5-10500H) with an input size of 640*640. The integration of these deep learning methods, utilizing both subjective OSDI questionnaires and objective ocular blinking videos, signifies a groundbreaking approach that enhances diagnostic accuracy for DED. The study anticipates transformative effects on DED diagnosis and improved patient outcomes as technology advances. Additionally, the research team introduces a user-friendly system for dry eye detection, named the "AI Dry Eye Analytic System," accessible at the URL "mini.ac.cn," demonstrating the practical implementation of the developed methodologies.