Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease

Background: The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains limited due to the lack of standardized image formats and analysis models. To overcome this issue, we used the Smart Eye Camera (SEC), a video-recordable slit-lamp device, and collected videos of the anterior segment of the eye. This study aimed to evaluate the accuracy of the AI algorithm in estimating the tear film breakup time and apply this model for the diagnosis of DED according to the Asia Dry Eye Society (ADES) DED diagnostic criteria. Methods: : Using the retrospectively corrected DED videos of 158 eyes from 79 patients, 22,172 frames were annotated by the DED specialist to label whether or not the frame had breakup. The AI algorithm was developed using the training dataset and machine learning. The DED criteria of the ADES was used to determine the diagnostic performance. Results: : The accuracy of tear film breakup time estimation was 0·789 (95% confidence interval (CI): 0·769–0·809), and the area under the receiver operating characteristic curve of this AI model was 0·877 (95% CI: 0·861–0·893). The sensitivity and specificity of this AI model for the diagnosis of DED was 0·778 (95% CI: 0·572–0·912) and 0·857 (95% CI: 0·564–0·866), respectively. Conclusions: : We successfully developed a novel AI-based diagnostic model for DED. Our diagnostic model has the potential to enable ophthalmology examination outside hospitals and clinics.