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

PURPOSE: 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 structure-function...

Descripción completa

Detalles Bibliográficos
Autores principales: Shigueoka, Leonardo Seidi, de Vasconcellos, José Paulo Cabral, Schimiti, Rui Barroso, Reis, Alexandre Soares Castro, de Oliveira, Gabriel Ozeas, Gomi, Edson Satoshi, Vianna, Jayme Augusto Rocha, Lisboa, Renato Dichetti dos Reis, Medeiros, Felipe Andrade, Costa, Vital Paulino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6281287/
https://www.ncbi.nlm.nih.gov/pubmed/30517157
http://dx.doi.org/10.1371/journal.pone.0207784

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6281287/
https://www.ncbi.nlm.nih.gov/pubmed/30517157
http://dx.doi.org/10.1371/journal.pone.0207784

Ejemplares similares