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

Abstract

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.

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

“…Aside from looking at the ocular surface imaging modalities, other important predictors of DED such as blinking patterns like incomplete blinking was also explored [ 57 , 58 ]. In a study of 100 patients (50 with DED and 50 with healthy eyes) by Zheng et al, blink videos were recorded using Keratography 5 M (K5M; Oculus Optikgeräte GmbH, Wetzlar, Germany) [ 59 ]. A U-Net model was then used to extract 30 frames per second (FPS) white light videos, and 8 FPS infrared light videos, which were then utilised to derive blink profiles.…”

“…A U-Net model was then used to extract 30 frames per second (FPS) white light videos, and 8 FPS infrared light videos, which were then utilised to derive blink profiles. It was found that blink videos with 30 FPS have higher accuracy in detecting incomplete blinking which was a sensitive indicator of DED [ 59 ]. Another important predictive factor of DED is meibomian gland dysfunction (MGD) which results in the disruption of the tear film lipid layer and increases the tear film evaporation rate [ 60 ].…”

Artificial intelligence-assisted diagnosis of ocular surface diseases

“…Through AI analysis, Jing et al (2022) have found a significant correlation between corneal nerve morphological changes in patients with dry eyes and intrinsic corneal aberrations, particularly higher-order aberrations. Zheng et al (2022) established a blink analysis model using AI to generate a blink profile, which provides a new method for evaluating incomplete blinking and diagnosing dry eye. The above research shows that the AI model has achieved remarkable results in the segmentation of MG morphology in patients with dry eye.…”

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

“…These methods enabled the accurate detection and quantitative measurement of the dry areas, which can be helpful for the evaluation of the severity of DED [ 71 , 72 ]. Recently, Zheng et al [ 73 ] developed a DL-based algorithm for blink analysis using videos recorded by a Keratograph ® 5M (Oculus Optikgeräte GmbH, Wetzlar, Germany). In this study, the frequency of the incomplete blinking measured using the AI model was closely associated with the signs and symptoms of DED, suggesting the potential of the DL algorithm as a diagnostic tool for DED [ 73 ].…”