Abstract
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 (R2) 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] R2 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.
Highlights
Diagnostic models using deep learning can play a role in overcoming the challenge of glaucoma underdiagnosis while maintaining a limited false positive rate[14]
We revealed the presence of significant pixel information on glaucoma and vertical cup-to-disc ratio (VCDR) outside the optic nerve head (ONH), reporting AUC values up to 0.88 and an R2 score of 37% in the most extreme setup, respectively
The most striking observation is that significant performance in glaucoma detection and VCDR estimation can be achieved without access to the ONH
Summary
Diagnostic models using deep learning can play a role in overcoming the challenge of glaucoma underdiagnosis while maintaining a limited false positive rate[14]. The use of end-to-end deep learning in glaucoma led to a high reference sensitivity of 97.60% at 85% specificity in a recent international c hallenge[34] Those results came at the cost of lower insights into the decision process of the predictive model, as image features are no longer manually crafted and selected. The majority of explainability s tudies[20,24,28] employed some form of o cclusion[36], a technique in which parts of the test images are perturbed, and the effect on performance recorded. They mainly report significant importance of areas within the ONH. Our findings provide hard evidence that deep learning utilizes information outside the ONH during glaucoma detection and VCDR estimation
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