Frank D Verbraak1, Michael D Abramoff2,3,4, Gonny C F Bausch5, Caroline Klaver6,7,8, Giel Nijpels9, Reinier O Schlingemann10, Amber A van der Heijden9. 1. Department of Ophthalmology, VU Medical Center, Amsterdam, the Netherlands f.verbraak@vumc.nl. 2. Department of Ophthalmology and Visual Sciences, University of Iowa Hospital & Clinics, Iowa City, IA. 3. VA Medical Center, Iowa City, IA. 4. IDx, Iowa City, IA. 5. Star-SHL, Rotterdam, the Netherlands. 6. Department of Ophthalmology, Erasmus Medical Center, Rotterdam, the Netherlands. 7. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands. 8. Department of Ophthalmology, Radboud University Medical Center, Rotterdam, the Netherlands. 9. Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands. 10. Department of Ophthalmology, Amsterdam Medical Center, Amsterdam, the Netherlands.
Abstract
OBJECTIVE: To determine the diagnostic accuracy in a real-world primary care setting of a deep learning-enhanced device for automated detection of diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS: Retinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning-enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more [mom]DR) was compared with a reference standard. This reference standard consisted of grading according to the International Clinical Classification of DR by the Rotterdam Study reading center. We determined the diagnostic accuracy of the hybrid deep learning-enhanced device (IDx-DR-EU-2.1) against the reference standard. RESULTS: A total of 1,616 people with type 2 diabetes were imaged. The hybrid deep learning-enhanced device's sensitivity/specificity against the reference standard was, respectively, for vtDR 100% (95% CI 77.1-100)/97.8% (95% CI 96.8-98.5) and for mtmDR 79.4% (95% CI 66.5-87.9)/93.8% (95% CI 92.1-94.9). CONCLUSIONS: The hybrid deep learning-enhanced device had high diagnostic accuracy for the detection of both vtDR (although the number of vtDR cases was low) and mtmDR in a primary care setting against an independent reading center. This allows its' safe use in a primary care setting.
OBJECTIVE: To determine the diagnostic accuracy in a real-world primary care setting of a deep learning-enhanced device for automated detection of diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS: Retinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning-enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more [mom]DR) was compared with a reference standard. This reference standard consisted of grading according to the International Clinical Classification of DR by the Rotterdam Study reading center. We determined the diagnostic accuracy of the hybrid deep learning-enhanced device (IDx-DR-EU-2.1) against the reference standard. RESULTS: A total of 1,616 people with type 2 diabetes were imaged. The hybrid deep learning-enhanced device's sensitivity/specificity against the reference standard was, respectively, for vtDR 100% (95% CI 77.1-100)/97.8% (95% CI 96.8-98.5) and for mtmDR 79.4% (95% CI 66.5-87.9)/93.8% (95% CI 92.1-94.9). CONCLUSIONS: The hybrid deep learning-enhanced device had high diagnostic accuracy for the detection of both vtDR (although the number of vtDR cases was low) and mtmDR in a primary care setting against an independent reading center. This allows its' safe use in a primary care setting.
Authors: Sebastian Paul; Allam Tayar; Ewa Morawiec-Kisiel; Beathe Bohl; Rico Großjohann; Elisabeth Hunfeld; Martin Busch; Johanna M Pfeil; Merlin Dähmcke; Tara Brauckmann; Sonja Eilts; Marie-Christine Bründer; Milena Grundel; Bastian Grundel; Frank Tost; Jana Kuhn; Jörg Reindel; Wolfgang Kerner; Andreas Stahl Journal: Ophthalmologie Date: 2022-01-26
Authors: Fernando Korn Malerbi; Rafael Ernane Andrade; Paulo Henrique Morales; José Augusto Stuchi; Diego Lencione; Jean Vitor de Paulo; Mayana Pereira Carvalho; Fabrícia Silva Nunes; Roseanne Montargil Rocha; Daniel A Ferraz; Rubens Belfort Journal: J Diabetes Sci Technol Date: 2021-01-12