Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity.

Abstract

To evaluate the accuracy of detecting glaucoma visual field defect severity using deep-learning (DL) classifier with an ultrawide-field scanning laser ophthalmoscope. One eye of 982 open-angle glaucoma (OAG) patients and 417 healthy eyes were enrolled. We categorized glaucoma patients into 3 groups according to the glaucoma visual field damage (Humphrey Field Analyzer 24-2 program) [early; -6 dB (mean deviation) or better, moderate; between -6 and -12 dB, and severe as mean deviation of -12 dB or worse]. In total, 558 images (446 for training and 112 for grading) from early OAG patients, 203 images (162 for training and 41 for grading) from moderate OAG patients, 221 images (176 for training and 45 for grading) from severe OAG patients and 417 images (333 for training and 84 for grading) from normal subjects were analyzed using DL. The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy after 100 trials. The mean AUC between normal versus all glaucoma patients was 0.872, the sensitivity was 81.3% and the specificity was 80.2%. In normal versus early OAG, mean AUC was 0.830, the sensitivity was 83.8% and the specificity was 75.3%. In normal versus moderate OAG, mean AUC was 0.864, sensitivity was 77.5%, and specificity was 90.2%. In normal versus severe OAG glaucoma mean AUC was 0.934, sensitivity was 90.9%, and specificity was 95.8%. Despite using an ultrawide-field scanning laser ophthalmoscope, DL can detect glaucoma characteristics and glaucoma visual field defect severity with high reliability.