A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

Dry Eye Disease (DED) is one of the commonly occurring chronic disease today, affecting the vision of eye. It causes severe discomfort in eye, visual disturbance and blurred vision impacting the quality of life of patients. Due to recent advancements in ArtificialIntelligence (AI) and rapid progress of analytics techniques, several image modality based computerized DED detection techniques are available in literature.These techniques transform image data into real and actionable insights and thus, provide new understanding and ways for better and quicker treatment.In addition, they assist ophthalmologist to diagnose dry eye disease precisely as well as to reduce the cost of healthcare.In this paper, we present a review on computerized DED detection techniques covering all available techniques.In addition, we present a parametric evaluation of these techniques based on our identified set of parameters.Though surveys on clinical DED detection techniques are available, this is the first attempt in literature to log down all computerized DED detection techniques at one place.Our survey will assist researchers and ophthalmologists to understand various technological advancements in the field of DED detection.As a little amount of work has been done in the field of automatizing dry eye disease detection, the field provides plenty of research opportunities to develop an automated efficient DED detection technique.