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A hybrid system integrating deep learning and computer vision for automated blink monitoring and tear film break-up
Yike Li1, An-Peng Pan2, Yiting Sun3
1Zhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, China; Zhejiang Provincial Key Laboratory of Ophthalmolgy, Zhejiang Provincial Clinical Research Center For Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases. No.1, Xihu Avenue, Hangzhou, 310009, Zhejiang, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
Purpose:
To construct and validate a hybrid system integrating deep learning and computer vision for real-time blink monitoring, tear film Break-Up Patterns (BUPs) classification, and Dry Eye (DE) subtype diagnosis and treatment recommendation.
Methods:
This prospective study included 95 subjects. Three YOLOv8 models (YOLOv8-n/s/m) were trained and compared to detect ocular structures and Tear Film Break Up (TFBU) regions. The best-performing model was integrated with OpenCV image processing algorithms based on geometric morphology to classify five specific BUPs (Area, Line, Spot, Dimple, and Random) and perform real-time blink monitoring. The overall performance of the automated system was validated on an independent test set.
Results:
Significant differences in Ocular Surface Disease Index (OSDI) and Tear-film Break Up Time (TBUT) were observed between the DE (n = 65) and non-DE (n = 30) groups (P < 0.001). The YOLOv8-m model was selected for its optimal performance (Precision: 74.6% for TFBU detection) to construct the automated blink monitoring and BUPs classification system. In the test set, this system achieved a 100% success rate in blink monitoring and an overall BUPs classification accuracy of 75.47%. System-measured TBUT (STBUT) and Manually-measured TBUT (MTBUT) demonstrated excellent agreement. Notably, STBUT (1.99 ± 1.36 s) was significantly shorter than MTBUT (2.21 ± 1.47 s, P < 0.01), reflecting the system's higher sensitivity in detecting early TFBU, alongside inherent methodological differences. Bland-Altman analysis confirmed that differences were predominantly distributed within the 95% limits of agreement.
Conclusion:
The hybrid system enables real-time blink monitoring and precise BUPs classification, supporting Tear Film-Oriented Diagnosis (TFOD) and personalized DE management.
