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Optical coherence tomography – machine learning
Author(s) -
Bernardes R.,
CasteloBranco M.
Publication year - 2017
Publication title -
acta ophthalmologica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.534
H-Index - 87
eISSN - 1755-3768
pISSN - 1755-375X
DOI - 10.1111/j.1755-3768.2017.01173
Subject(s) - optical coherence tomography , computer science , set (abstract data type) , artificial intelligence , retina , machine learning , data set , neuroscience , optics , psychology , physics , programming language
Summary Machine learning is a method of data analysis. It gives computers the ability to learn from data and to build models from which predictions can be made. Even though OCT is mainly used to visualize retinal structures and compute thickness maps, it conveys information on subtle changes within the retina before structural ones can be identified. Exposing the machine learning model to a set of examples allows it to build a model from which predictions can be made on new data and explain differences between groups. This lecture will explain the underlying principles of machine learning and discuss potential applications in ophthalmology and CNS disorders through the imaging of the retina.