Back to Search
Start Over
Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.
- Source :
-
American journal of ophthalmology [Am J Ophthalmol] 2020 Mar; Vol. 211, pp. 123-131. Date of Electronic Publication: 2019 Nov 12. - Publication Year :
- 2020
-
Abstract
- Purpose: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs.<br />Design: Evaluation of a machine learning algorithm.<br />Methods: An M2M DL algorithm trained with RNFL thickness parameters from spectral-domain optical coherence tomography was applied to a subset of 490 fundus photos of 490 eyes of 370 subjects graded by 2 glaucoma specialists for the probability of glaucomatous optical neuropathy (GON), and estimates of cup-to-disc (C/D) ratios. Spearman correlations with standard automated perimetry (SAP) global indices were compared between the human gradings vs the M2M DL-predicted RNFL thickness values. The area under the receiver operating characteristic curves (AUC) and partial AUC for the region of clinically meaningful specificity (85%-100%) were used to compare the ability of each output to discriminate eyes with repeatable glaucomatous SAP defects vs eyes with normal fields.<br />Results: The M2M DL-predicted RNFL thickness had a significantly stronger absolute correlation with SAP mean deviation (rho=0.54) than the probability of GON given by human graders (rho=0.48; P < .001). The partial AUC for the M2M DL algorithm was significantly higher than that for the probability of GON by human graders (partial AUC = 0.529 vs 0.411, respectively; P = .016).<br />Conclusion: An M2M DL algorithm performed as well as, if not better than, human graders at detecting eyes with repeatable glaucomatous visual field loss. This DL algorithm could potentially replace human graders in population screening efforts for glaucoma.<br /> (Copyright © 2019 Elsevier Inc. All rights reserved.)
- Subjects :
- Aged
Algorithms
Area Under Curve
Cross-Sectional Studies
Female
Fundus Oculi
Glaucoma, Open-Angle diagnostic imaging
Gonioscopy
Humans
Intraocular Pressure physiology
Male
Middle Aged
Optic Nerve Diseases diagnostic imaging
Photography
ROC Curve
Retrospective Studies
Tomography, Optical Coherence
Vision Disorders diagnosis
Visual Field Tests methods
Visual Fields physiology
Deep Learning
Glaucoma, Open-Angle diagnosis
Nerve Fibers pathology
Optic Nerve Diseases diagnosis
Physical Examination
Retinal Ganglion Cells pathology
Subjects
Details
- Language :
- English
- ISSN :
- 1879-1891
- Volume :
- 211
- Database :
- MEDLINE
- Journal :
- American journal of ophthalmology
- Publication Type :
- Academic Journal
- Accession number :
- 31730838
- Full Text :
- https://doi.org/10.1016/j.ajo.2019.11.006