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

no code implementations • 11 May 2022 • Verónica Sánchez-Gutiérrez, Paula Hernández-Martínez, Francisco J. Muñoz-Negrete, Jonne Engelberts, Allison M. Luger, Mark J. J. P. van Grinsven

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.

Specificity

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration

no code implementations • 22 Mar 2019 • Cristina González-Gonzalo, Verónica Sánchez-Gutiérrez, Paula Hernández-Martínez, Inés Contreras, Yara T. Lechanteur, Artin Domanian, Bram van Ginneken, Clara I. Sánchez

Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v. 1. 3. 0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases.