An Overview of Dry Eye Analysis Algorithms for Tear Film Break-Up Time Detection

Abstract

Nowadays, one of the most common chronic diseases is dry eye. It causes extreme eye pain, visual interference, and hazy eyes affecting patients' life quality. Along with recent developments in Artificial Intelligence as well as the rapid advancement of statistical methods, many computerized techniques are available for detecting dry eye conditions based on image modality. These strategies convert image data into real and usable findings, allowing for better and quicker treatment for new insight and approaches. They also help ophthalmologists accurately identify dry eye diseases and reduce healthcare costs. This paper provides an overview of the algorithms for analyzing dry eye diseases through Tear Film Break-Up Time (TFBUT) requires instillation of fluorescein solution in the eye on Deep Convolutional Neural Network (CNN), Random Sample Consensus (RANSAC) segmentation, morphological operation for rupture pattern and histogram based.