From the American Academy of Ophthalmology’s 126th annual meeting (AAO 2022), Eyenuk, Inc. a global artificial intelligence (AI) digital health company and the leader in real-world applications for AI Eye Screening, announced the publication of strong EyeArt validation results in Ophthalmology Science, a peer-reviewed journal of the American Academy of Ophthalmology.
The study, titled “Artificial Intelligence Detection of Diabetic Retinopathy: Subgroup Comparison of the EyeArt System with Ophthalmologists’ Dilated Exams,” evaluated general ophthalmologists, retina specialists, and Eyenuk‘s EyeArt AI system for detecting diabetic retinopathy (DR), a leading cause of blindness among working-age adults.
The EyeArt AI system did not miss any vision-threatening DR, although general ophthalmologists missed a few instances.
Professor Jennifer I. Lim MD, Vice Chair of Ophthalmology, UIC Distinguished Professor of Ophthalmology and Director of the Retina Service at The University of Illinois at Chicago and the first author on the publication commented on the results, “As compared to the Reading Center grading, which was the reference standard, the sensitivity for detection of more than mild DR was significantly greater with the EyeArt AI system than with either a general ophthalmologist or a retina specialist clinical examination. Unlike a few instances in which general ophthalmologists missed some cases of vision-threatening diabetic retinopathy, the EyeArt AI system did not miss any cases of vision-threatening DR. The AI system is a significant tool to help us tackle the burden of DR screening and detection of DR in a timely manner.”
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The study evaluated the sensitivity and specificity of the EyeArt system and dilated eye exams performed by general ophthalmologists and retina specialists against the rigorous Early Treatment Diabetic Retinopathy Study (ETDRS) clinical reference standard on the same cohort of 521 study participants. The ETDRS reference standard was established by experts at the University of Wisconsin Reading Center using 10 fundus images per eye captured after dilation by certified photographers, whereas the EyeArt system only analyzed 2 images per eye, typically without dilation.