Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma.

Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma.

<h4>Purpose</h4>To test the ability of machine learning classifiers (MLCs) using optical coherence tomography (OCT) and standard automated perimetry (SAP) parameters to discriminate between healthy and glaucomatous individuals, and to compare it to the diagnostic ability of the combined...

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Bibliographic Details
Main Authors: Leonardo Seidi Shigueoka, José Paulo Cabral de Vasconcellos, Rui Barroso Schimiti, Alexandre Soares Castro Reis, Gabriel Ozeas de Oliveira, Edson Satoshi Gomi, Jayme Augusto Rocha Vianna, Renato Dichetti Dos Reis Lisboa, Felipe Andrade Medeiros, Vital Paulino Costa
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0207784

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https://doi.org/10.1371/journal.pone.0207784

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