Deep learning on fundus images detects glaucoma beyond the optic disc

Hemelings, Ruben; Elen, Bart; Barbosa-Breda, João; Blaschko, Matthew B.; De Boever, Patrick; Stalmans, Ingeborg

Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma detection and vertical cup-disc ratio (VCDR), an important risk factor. We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy. We defined the crop radius as a percentage of image size, centered on the optic nerve head (ONH), with an equidistant spaced range from 10–60% (ONH crop policy). The inverse of the cropping mask was also applied (periphery crop policy). Trained models using original images resulted in an area under the curve (AUC) of 0.94 [95% CI 0.92–0.96] for glaucoma detection, and a coefficient of determination (R(2)) equal to 77% [95% CI 0.77–0.79] for VCDR estimation. Models that were trained on images with absence of the ONH are still able to obtain significant performance (0.88 [95% CI 0.85–0.90] AUC for glaucoma detection and 37% [95% CI 0.35–0.40] R(2) score for VCDR estimation in the most extreme setup of 60% ONH crop). Our findings provide the first irrefutable evidence that deep learning can detect glaucoma from fundus image regions outside the ONH.