Artificial Intelligence in Keratoconus Diagnosis

Abstract

Artificial intelligence aims to develop machines capable of simulating the human ability to think and act. Machine learning can be defined as the branch of artificial intelligence that aims to endow the machine with the ability to learn. Developing an efficient technique to assist practitioners in objectively detecting early keratoconus is of paramount importance. Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. This is a burgeoning field of study, with significant potential for continued advancement as screening devices and techniques become more sophisticated. Understanding these aspects and the revolution we are experiencing is important to guide the construction process and the validation of these decision-making assistant algorithms before deploying them to patient care.