Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Open Access
- 5 December 2018
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 13 (12), e0207784
- https://doi.org/10.1371/journal.pone.0207784
Abstract
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. Cross-sectional prospective study. 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 glaucoma specialists exposed to the same OCT and SAP data. The AUCs obtained with MLCs ranged from 0.805 (Classification Tree) to 0.931 (Radial Basis Function Network, RBF). The sensitivity at 90% specificity ranged from 51.6% (Classification Tree) to 82.8% (Bagging, Multilayer Perceptron and Support Vector Machine Gaussian). The CSFI had a sensitivity of 79.3% at 90% specificity, and the highest AUC (0.948). General ophthalmologists and glaucoma specialists’ grading had sensitivities of 66.2% and 83.8% at 90% specificity, and AUCs of 0.879 and 0.921, respectively. RBF (the best MLC), the CSFI, and glaucoma specialists showed significantly higher AUCs than that obtained by general ophthalmologists (P0.25). Our findings suggest that both MLCs and the CSFI can be helpful in clinical practice and effectively improve glaucoma diagnosis in the primary eye care setting, when there is no glaucoma specialist available.Keywords
Funding Information
- Fundação de Amparo à Pesquisa do Estado de São Paulo (07/51281-9)
This publication has 51 references indexed in Scilit:
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Ability of Cirrus HD-OCT Optic Nerve Head Parameters to Discriminate Normal from Glaucomatous EyesOphthalmology, 2011
- Linking structure and function in glaucomaProgress in Retinal and Eye Research, 2010
- Combining Functional and Structural Tests Improves the Diagnostic Accuracy of Relevance Vector Machine ClassifiersJournal of Glaucoma, 2010
- Comparison of Quantitative Imaging Devices and Subjective Optic Nerve Head Assessment by General Ophthalmologists to Differentiate Normal From Glaucomatous EyesJournal of Glaucoma, 2009
- Accuracy of dementia diagnosis--a direct comparison between radiologists and a computerized methodBrain, 2008
- A framework for comparing structural and functional measures of glaucomatous damageProgress in Retinal and Eye Research, 2007
- Improving Glaucoma Diagnosis by the Combination of Perimetry and HRT MeasurementsJournal of Glaucoma, 2006
- Visual Field Defects and Retinal Ganglion Cell Losses in Patients With GlaucomaAmerican Journal of Ophthalmology, 2006