The FDA has approved marketing of the IDx-DR (IDx), the first medical device to use artificial intelligence to detect greater than a mild level of diabetic retinopathy in adults.

“Early detection of retinopathy is an important part of managing care for the millions of people with diabetes, yet many patients with diabetes are not adequately screened for diabetic retinopathy since about 50% of them do not see their eye doctor on a yearly basis,” said Malvina Eydelman, MD, director of the Division of Ophthalmic and Ear, Nose, and Throat Devices at the FDA's Center for Devices and Radiological Health. “(This) decision permits the marketing of a novel artificial intelligence technology that can be used in a primary care doctor’s office.”

The device is based on a software program that uses an artificial intelligence algorithm to analyze images of the eye taken with a Topcon NW400 retinal camera. A doctor uploads the digital images of the patient’s retinas to a cloud server on which IDx-DR software is installed. If the images are of sufficient quality, the software provides the doctor with one of two results: (1) “more than mild diabetic retinopathy detected: refer to an eye care professional” or (2) “negative for more than mild diabetic retinopathy; rescreen in 12 months.” If a positive result is detected, patients should see an eye care provider for further diagnostic evaluation and possible treatment as soon as possible. 

IDx-DR is the first device authorized for marketing that provides a screening decision without the need for a clinician to also interpret the image or results, which makes it usable by health care providers who may not normally be involved in eye care.

The FDA evaluated data from a clinical study of retinal images obtained from 900 patients with diabetes at 10 primary care sites. The study was designed to evaluate how often IDx-DR could accurately detect patients with more than mild diabetic retinopathy. In the study, IDx-DR was able to correctly identify the presence of more than mild diabetic retinopathy 87.4% of the time and was able to correctly identify those patients who did not have more than mild diabetic retinopathy 89.5% of the time.