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

ABSTRACT:

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 index (CSFI), general ophthalmologists and glaucoma specialists.

Design

Cross-sectional prospective study.

Methods

Fifty eight eyes of 58 patients with early to moderate glaucoma (median value of the mean deviation = -3.44 dB; interquartile range, -6.0 to -2.4 dB) and 66 eyes of 66 healthy individuals underwent OCT and SAP tests. The diagnostic accuracy (area under the ROC curve-AUC) of 10 MLCs was compared to those obtained with the CSFI, 3 general ophthalmologists and 3 gl

SUBMITTER: Shigueoka LS 

PROVIDER: S-EPMC6281287 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature