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Title
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Deep learning on fundus images detects glaucoma beyond the optic disc
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Scientific reports. - London, 2011, currens
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Publication
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London
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Nature Publishing Group
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2021
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ISSN
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2045-2322
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DOI
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10.1038/S41598-021-99605-1
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Volume/pages
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11
:1
(2021)
, 12 p.
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Article Reference
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20313
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ISI
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000707032500063
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Pubmed ID
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34645908
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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