Work

Response to Comment on Lee et al. Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems. Diabetes Care 2021;44:1168–1175

Year: 2020

Type: article

Abstract: We thank Dr. Soliz for his expedient comments (1) regarding our work evaluating seven artificial intelligence (AI) algorithms in a head-to-head fashion (2). We welcome the opportunity to clarify these points of concern. We agree that the two U.S. Food and Drug Administration–approved diabetic retinopathy (DR) systems, one of which was tested in our study, use a threshold of moderate nonproliferative DR (NPDR) or higher for referral. We set the threshold to any DR versus no DR because that represents the clinical practice at the Veterans Affairs (VA) teleretinal system, as stated in our methods. All participating companies were aware of the study threshold prior to submitting their algorithms and were allowed to adjust their algorithms accordingly. As the VA manages one of the largest teleretinal screening systems in the U.S., …

Source: Diabetes Care

DOI: 10.2337/dci21-0007

Authors: Aaron Y. Lee, Cecilia S. Lee, Matthew S. Hunt, Ryan T. Yanagihara, Marian Blazes, Edward J. Boyko

Institutions: University of Washington, VA Puget Sound Health Care System, United States Department of Veterans Affairs, Seattle Epidemiologic Information and Research Center

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Research to Prevent Blindness, National Institutes of Health, National Institute on Aging, National Eye Institute