Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings

• URL: http://arxiv.org/abs/2205.05554v1
• Date: Wed, 11 May 2022 14:59:10 GMT
• Title: Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings
• Authors: Ver\'onica S\'anchez-Guti\'errez, Paula Hern\'andez-Mart\'inez, Francisco J. Mu\~noz-Negrete, Jonne Engelberts, Allison M. Luger, Mark J.J.P. van Grinsven
• Abstract summary: RetCAD v.1.3.1 was developed to automatically detect referable diabetic retinopathy (DR) Analysed the reduction of workload that can be released incorporating this artificial intelligence-based technology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Background: To determine the ability of a commercially available deep learning system, RetCAD v.1.3.1 (Thirona, Nijmegen, The Netherlands) for the automatic detection of referable diabetic retinopathy (DR) on a dataset of colour fundus images acquired during routine clinical practice in a tertiary hospital screening program, analyzing the reduction of workload that can be released incorporating this artificial intelligence-based technology. Methods: Evaluation of the software was performed on a dataset of 7195 nonmydriatic fundus images from 6325 eyes of 3189 diabetic patients attending our screening program between February to December of 2019. The software generated a DR severity score for each colour fundus image which was combined into an eye-level score. This score was then compared with a reference standard as set by a human expert using receiver operating characteristic (ROC) curve analysis. Results: The artificial intelligence (AI) software achieved an area under the ROC curve (AUC) value of 0.988 [0.981:0.993] for the detection of referable DR. At the proposed operating point, the sensitivity of the RetCAD software for DR is 90.53% and specificity is 97.13%. A workload reduction of 96% could be achieved at the cost of only 6 false negatives. Conclusions: The AI software correctly identified the vast majority of referable DR cases, with a workload reduction of 96% of the cases that would need to be checked, while missing almost no true cases, so it may therefore be used as an instrument for triage.

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