Impact of Incomplete Blinking Analyzed Using a Deep Learning Model With the Keratograph 5M in Dry Eye Disease.

Purpose: To establish a deep learning model (DLM) for blink analysis, and investigate whether blink video frame sampling rate influences the accuracy of analysis.

Methods: This case-controlled study recruited 50 dry eye disease (DED) participants and 50 normal subjects. Blink videos recorded by a Keratograph 5M, symptom questionnaires, and ocular surface assessments were collected. After processing the blink images as datasets, further training and evaluation of DLM was performed. Blink videos of 30 frames per second (FPS) under white light, eight FPS extracted from white light videos, and eight FPS under infrared light were processed by DLM to generate blink profiles, allowing comparison of blink parameters, and their association with DED symptoms and signs.

Results: The blink parameters based on 30 FPS video presented higher sensitivity and accuracy than those based on eight FPS. The average relative interpalpebral height (IPH), the frequency and proportion of incomplete blinking (IB) were much higher in DED participants than in normal controls (P < 0.001). The IB frequency was closely associated with DED symptoms and signs (|R| ≥ 0.195, P ≤ 0.048), as was IB proportion and the average IPH (R ≥ 0.202, P ≤ 0.042).

Conclusions: DLM is a powerful tool for analyzing blink videos with high accuracy and sensitivity, and a frame rate ≥ 30 FPS is recommended. The IB frequency is indicative of DED.

Translational Relevance: The system of DLM-based blink analysis is of great potential for the assessment of IB and diagnosis of DED.