Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting
Frank D. Verbraak,¹ Michael D. Abramoff,²³⁴ Gonny C.F. Bausch,⁵ Caroline Klaver,⁶⁷⁸ Giel Nijpels,⁹ Reinier O. Schlingemann,¹⁰ and Amber A. van der Heijden,⁹
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).
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.