High interpretable machine learning classifier for early glaucoma diagnosis

High interpretable machine learning classifier for early glaucoma diagnosis

AIM: To develop a classifier for differentiating between healthy and early stage glaucoma eyes based on peripapillary retinal nerve fiber layer (RNFL) thicknesses measured with optical coherence tomography (OCT), using machine learning algorithms with a high interpretability. METHODS: Ninety patient...

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Bibliographic Details
Main Authors: Carlos Salvador Fernandez Escamez, Elena Martin Giral, Susana Perucho Martinez, Nicolas Toledano Fernandez
Format: Article
Language:English
Published: Press of International Journal of Ophthalmology (IJO PRESS) 2021-03-01
Series:International Journal of Ophthalmology
Subjects:
machine learning
glaucoma
diagnosis
optical coherence tomography
Online Access:http://ies.ijo.cn/en_publish/2021/3/20210310.pdf

Internet

http://ies.ijo.cn/en_publish/2021/3/20210310.pdf

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