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Purpose: Develop a deep learning-based automated method to segment meibomianglands (MG) and eyelids, quantitatively analyze the MG area and MG ratio,estimate the meiboscore, and remove specular reflections from infrared images.Methods: A total of 1600 meibography images were captured in a clinicalsetting. 1000 images were precisely annotated with multiple revisions byinvestigators and graded 6 times by meibomian gland dysfunction (MGD) experts.Two deep learning (DL) models were trained separately to segment areas of theMG and eyelid. Those segmentation were used to estimate MG ratio andmeiboscores using a classification-based DL model. A generative adversarialnetwork was implemented to remove specular reflections from original images.Results: The mean ratio of MG calculated by investigator annotation and DLsegmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34%vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01%accuracy for meiboscore classification on validation set and 59.17% accuracywhen tested on images from independent center, compared to 53.44% validationaccuracy by MGD experts. The DL-based approach successfully removes reflectionfrom the original MG images without affecting meiboscore grading. Conclusions:DL with infrared meibography provides a fully automated, fast quantitativeevaluation of MG morphology (MG Segmentation, MG area, MG ratio, andmeiboscore) which are sufficiently accurate for diagnosing dry eye disease.Also, the DL removes specular reflection from images to be used byophthalmologists for distraction-free assessment.

AI-based automated Meibomian gland segmentation, classification and reflection correction in infrared Meibography