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Title: | Deep learning on fundus images detects glaucoma beyond the optic disc | Authors: | Hemelings, Ruben Elen, Bart Barbosa-Breda, Joao Blaschko, Matthew B. DE BOEVER, Patrick Stalmans, Ingeborg |
Issue Date: | 2021 | Publisher: | NATURE PORTFOLIO | Source: | Scientific reports (Nature Publishing Group), 11 (1) (Art N° 20313) | 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 (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. | Notes: | Hemelings, R (corresponding author), Katholieke Univ Leuven, Res Grp Ophthalmol, Dept Neurosci, Herestr 49, B-3000 Leuven, Belgium.; Hemelings, R (corresponding author), Flemish Inst Technol Res VITO, Boeretang 200, B-2400 Mol, Belgium. ruben.hemelings@kuleuven.be |
Document URI: | http://hdl.handle.net/1942/35912 | ISSN: | 2045-2322 | e-ISSN: | 2045-2322 | DOI: | 10.1038/s41598-021-99605-1 | ISI #: | WOS:000707032500063 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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s41598-021-99605-1.pdf | Published version | 1.6 MB | Adobe PDF | View/Open |
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