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

@article{SanchezGutierrez2022PerformanceOA,
  title={Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings},
  author={Ver'onica S'anchez-Guti'errez and Paula Hern'andez-Mart'inez and Francisco Jos{\'e} Mu{\~n}oz-Negrete and Jonne Engelberts and Allison M. Luger and Mark J. J. P. van Grinsven},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.05554}
}
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… 
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