Ipp E, Liljenquist D, Bode B, et al. Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy. JAMA Netw Open. 2021;4(11):e2134254. Published 2021 Nov 15. doi:10.1001/jamanetworkopen.2021.34254
To evaluate the safety and accuracy of the EyeArt® autonomous artificial intelligence (AI) diabetic retinopathy (DR) detection system (version 2.1.0) in detecting both more than mild DR (mtmDR) and vision-threatening DR (vtDR).
Design: Prospective multicenter cross-sectional diagnostic study registered with ClinicalTrials.gov (NCT03112005)
Study Population: 942 individuals 18 years or older with diabetes across 15 US study centers (6 primary care, 9 eye care)
Reference Standard: Early Treatment Diabetic Retinopathy Study (ETDRS) grading of 4-wide-field stereoscopic dilated fundus photographs (equivalent to 7-field 30° ETDRS photographs) by the Wisconsin Reading Center (WRC). Two independent certified graders masked to the EyeArt autonomous AI system’s results examined the 4-wide field photographs to establish the reference standard. Between-grader differences exceeding prespecified criteria were adjudicated by a third, more senior grader.
The EyeArt autonomous AI System can accurately detect vtDR and mtmDR without physician oversight or need for dilation in most individuals, facilitating diabetic eye examinations at non-specialist facilities and enabling accelerated referral of vtDR.