Introduction to Machine Learning for Ophthalmologists

Abstract

New diagnostic and imaging techniques generate such an incredible amount of data that it is often a challenge to extract all information that could be possibly useful in clinical practice. Machine Learning techniques emerged as an objective tool to assist practitioners to diagnose certain conditions and take clinical decisions. In particular, Machine Learning techniques have repeatedly shown their usefulness for ophthalmologists. The possible applications of this technology go much further than been used as diagnostic tool, as it may also be used to grade the severity of a pathology, perform early disease detection, or predict the evolution of a condition. This work reviews not only the latest achievements of Machine Learning in ocular sciences, but also aims to be a comprehensive and concise overview of all steps of the process, with clear and easy explanation for each technical term, focusing on the basic knowledge required to understand Machine Learning.

Keywords: Automated diagnosis; artificial intelligence; machine learning; neural networks; vision sciences.