Study shows AI can deliver specialty-level diagnosis in primary care setting
A system designed by a University of Iowa ophthalmologist that uses artificial intelligence (AI) to detect diabetic retinopathy without a person interpreting the results earned Food and Drug Administration (FDA) authorization in April, following a clinical trial in primary care offices. Results of that study were published Aug. 28 online in Nature Digital Medicine, offering the first look at data that led to FDA clearance for IDx-DR, the first medical device that uses AI for the autonomous detection of diabetic retinopathy.
The clinical trial, which also was the first study to prospectively assess the safety of an autonomous AI system in patient care, compared the performance of IDx-DR to the gold standard diagnostic for diabetic retinopathy, which is the leading cause of vision loss in adults and one of the most severe complications for the 30.3 million Americans living with diabetes.
IDx-DR exceeded all pre-specified superiority endpoints in sensitivity, the ability to correctly identify a patient with disease; specificity, the ability to correctly classify a person as disease-free; and imageability, or the capability to produce quality images of the retina and determine the severity of the disease.
More than 24,000 people in the U.S. lose their sight to diabetic retinopathy each year. Early detection and treatment can reduce the risk of blindness by 95%, but less than 50% of patients with diabetes schedule regular exams with an eye-care specialist.
In the study, 900 adult patients with diabetes—but no history of diabetic retinopathy—were examined at 10 primary care sites across the U.S. Retinal images of the patients were obtained using a robotic camera, with an AI assisting the operator in getting good quality images. Once the four images were complete, the diagnostic AI then made a clinical diagnosis in 20 seconds. The diagnostic AI detects disease just as expert clinicians do, by having detectors for the lesions characteristic for diabetic retinopathy, including microaneurysms, hemorrhages, and lipoprotein exudates.
Camera operators in the study were existing staff of the primary care clinics, but not physicians or trained photographers.
Study participants also had retinal images taken at each of the primary care clinics using specialized widefield and 3D imaging equipment without AI operated by experienced retinal photographers certified by the Wisconsin Fundus Photograph Reading Center (FPRC)—the gold standard in grading the severity of diabetic retinopathy.
Complete diagnostic data accomplished by both the AI system and FPRC readers was available for 819 of the original 900 study participants. FPRC readers identified 198 participants with more than mild diabetic retinopathy who should be further examined by a specialist; the AI was able to correctly identify 173 of the 198 participants with disease, resulting in a sensitivity of 87%. Among the 621 disease-free participants identified by FPRC readers, AI identified 556 participants, for a specificity of 90%. The AI had a 96% imageability rate: Of the 852 participants who had an FPRC diagnosis, 819 had an AI system diagnostic output.
In June, following FDA clearance, providers at the Diabetes and Endocrinology Center at UI Health Care-Iowa River Landing in Coralville, Iowa, were the first in the nation to begin using IDx-DR to screen patients.
