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
• vision sciences
• machine learning
• neural networks

ACKNOWLEDGMENTS

This work was supported by a grant from the Flemish Fund for Scientific Research (TBM - T000416N).

DISCLOSURE STATEMENT

The authors report no conflicts of interest and have no proprietary interest in any of the materials mentioned in this article.

Additional information

Funding

This work was supported by the Flemish Fund for Scientific Research [TBM - T000416N].