Abstract
Artificial Intelligence (AI) has long promised to increase healthcare affordability, quality and accessibility but FDA, until recently, had never authorized an autonomous AI diagnostic system. This pivotal trial of an AI system to detect diabetic retinopathy (DR) in people with diabetes enrolled 900 subjects, with no history of DR at primary care clinics, by comparing to Wisconsin Fundus Photograph Reading Center (FPRC) widefield stereoscopic photography and macular Optical Coherence Tomography (OCT), by FPRC certified photographers, and FPRC grading of Early Treatment Diabetic Retinopathy Study Severity Scale (ETDRS) and Diabetic Macular Edema (DME). More than mild DR (mtmDR) was defined as ETDRS level 35 or higher, and/or DME, in at least one eye. AI system operators underwent a standardized training protocol before study start. Median age was 59 years (range, 22–84 years); among participants, 47.5% of participants were male; 16.1% were Hispanic, 83.3% not Hispanic; 28.6% African American and 63.4% were not; 198 (23.8%) had mtmDR. The AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% (95% CI, 81.8–91.2%) (>85%), specificity of 90.7% (95% CI, 88.3–92.7%) (>82.5%), and imageability rate of 96.1% (95% CI, 94.6–97.3%), demonstrating AI’s ability to bring specialty-level diagnostics to primary care settings. Based on these results, FDA authorized the system for use by health care providers to detect more than mild DR and diabetic macular edema, making it, the first FDA authorized autonomous AI diagnostic system in any field of medicine, with the potential to help prevent vision loss in thousands of people with diabetes annually. ClinicalTrials.gov NCT02963441
Highlights
People with diabetes fear visual loss and blindness more than any other complication.[1]
1234567890():,; Pivotal trial of an autonomous Artificial intelligence (AI)-based diagnostic system MD Abràmoff et al In this study, we evaluate the diagnostic performance of an 304 μm (±0.06 μm) in the participants with center-involved Optical Coherence Tomography (OCT)
The enrichment strategy led to 319 enriched participants; the 621 No/Mild Diabetic retinopathy (DR) participants included 218 participants from enrichment while the 198 More than mild DR (mtmDR) participants included 101 participants from enrichment
Summary
People with diabetes fear visual loss and blindness more than any other complication.[1] Diabetic retinopathy (DR) is the primary cause of blindness and visual loss among working age men and women in the United States and causes more than 24,000 people to lose vision each year.[2,3] Adherence to regular eye examinations is necessary to diagnose DR at an early stage, when it can be treated with the best prognosis,[4,5] and have resulted in substantial reductions in visual loss and blindness.[6] Despite this, less than. 50% of patients with diabetes adhere to the recommended schedule of eye exams,[7] and adherence has not increased over the last 15 years despite large-scale efforts to increase it.[8] To increase adherence, retinal imaging in or close to primary care offices followed by remote evaluation using telemedicine has been widely studied.[9,10,11]. Artificial intelligence (AI)-based algorithms to detect DR from retinal images have been examined in laboratory settings.[12,13,14,15]
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