In the study, 900 patients with diabetes but no history of diabetic retinopathy were examined at 10 primary care sites across the US and retinal images of the patients were obtained using a robotic camera, with an AI assisting the operator in getting good quality images. Study participants also had retinal images taken at each of the primary care clinics using specialised widefield and 3D imaging equipment without AI operated by experienced retinal photographers certified by the Wisconsin Fundus Photograph Reading Center. The AI was able to correctly identify 173 of the 198 participants with the disease, resulting in a sensitivity of 87 per cent.
Indeed, IDx-DR was found to exceed all endpoints in the ability to correctly identify a patient with disease, the ability to correctly classify a person as disease-free, and the capability to produce quality images of the retina and determine the severity of the disease.
“The AI system's primary role is to identify those people with diabetes who are likely to have diabetic retinopathy that requires further evaluation by an eye-care provider. The study results demonstrate the safety of autonomous AI systems to bring specialty-level diagnostics to a primary care setting, with the potential to increase access and lower cost,” said Dr Michael Abràmoff, the Robert C. Watzke Professor of Ophthalmology and Visual Sciences with UI Health Care and Principal Investigator on the study. He is also Founder and President of IDx, the company that created the IDx-DR system and funded the study. IDx is working with the American Medical Association to ensure that there is clear coding guidance for billing of IDx-DR.