Tear Film Break-Up Time Measurement Using Deep Convolutional Neural Networks for Screening Dry Eye Disease

Abstract

Tear film instability is one of the major characteristics of dry eye syndrome. However, traditional diagnostic methods, such as the fluorescein tear film break-up time (FTBUT) test, are limited by the subjective interpretation of results. The test needs to manually identify break-up areas in the fluorescent image, thus producing variable diagnosis results. This paper proposes an automatic method to detect the fluorescent tear film break-up area using a deep convolutional neural network (CNN) model and to define its appearance as CNN-BUT. A digital slit-lamp recorded the standard FTBUT measurement for each of 80 study participants. Fifty participants were used to train the CNN model to identify the tear film break-up area, while the remaining 30 were used to validate the proposed CNN-BUT test. Among six normal controls and 24 dry eye patients enrolled in this paper, CNN-BUT was significantly lower in dry eye patients ( ${p} ). The correlation between CNN-BUT and FTBUT was also significant ( ${r} =0.9$ ; ${p} ). Using 5 s as the cutoff value, the CNN-BUT offered acceptable sensitivity and specificity to screen dry eye patients (0.83 and 0.95, respectively). These results indicate that CNN-BUT may be used to evaluate tear film stability and to assess the status of dry eye syndrome automatically.