Project Summary Dry eye (DE) is a highly prevalent condition with significant impacts on individuals and society that continues to evade easy diagnosis and treatment. The most common cause of DE is thought to be Meibomian gland dysfunction (MGD). The Meibomian glands in the upper and lower eyelids secrete lipids that form a thin film covering the aqueous tears and inhibit their evaporation. In MGD, it is thought that inadequate and/or poor quality tear lipids are secreted, leading to tear film instability, evaporation, and symptoms of DE. The glandular changes that occur in MGD are not well understood, nor are we able to identify which aspects of MGD pose the greatest risk for tear film instability and DE. The Dry Eye Assessment and Management (DREAM) Study was a clinical trial of ω3 fatty acid supplements for the treatment of DE. Over the course of the trial a large database of meibography images – infrared images of the everted eyelids that reveal the Meibomian glands – was compiled and analyzed using a novel scheme to characterize 13 different aspects of the glands by visual inspection and analyze their relationships to the clinically assessed quality of the secreted lipids. The process was arduous and time consuming, inherently subject to human bias, and provided little new information on the links between Meibomian gland characteristics and DE signs and symptoms. Recent advances in artificial intelligence (AI) have allowed us to train supervised machine learning algorithms on meibography images to automatically detect and quantify detailed morphological features of the individual glands. These detailed morphological features potentially contain a wealth of information about the health and functioning of the Meibomian glands, and could provide valuable information on the mechanisms behind MGD and its clinical implications. A further emerging AI technology for use in medical imaging – unsupervised discriminative feature learning – mitigates the human bias, and could potentially discover previously unidentified properties in meibography images, and possible links to crucial clinical endpoints like tear film instability and DE symptoms. In this project, we propose to utilize this new AI technology to re-analyze the DREAM Study clinical database of meibography images to dramatically extend their initial findings. Specifically, we will employ unsupervised discriminative feature learning to mitigate the human bias in meibography analysis, discover previously unrecognized features of the Meibomian glands, and to analyze the links between these features and MGD, tear film instability, and the clinical signs and symptoms indicative of DE.