Schedule a Meeting

Some systems are taught what to look for in order to detect the presence of disease; other systems train themselves.

It’s become clear that computerized image analysis can be a powerful tool for helping to diagnose some diseases, including diabetic retinopathy and some types of macular degeneration. But one question that remains to be answered is how the artificial intelligence should learn what to look for. One approach is to teach the software to analyze and quantify specific known signs of the disease, much as a human specialist would do. The other approach is to allow the system to determine on its own how to identify healthy vs. diseased eyes by showing it a large number of samples of each. Both approaches have shown significant promise.

The First Approved System

The first-ever AI diagnostic system to obtain FDA approval is the LumineticsCore™ (formerly known as IDx-DR) system from IDx (Coralville, Iowa). The LumineticsCore (formerly known as IDx-DR) is designed to analyze retinal photos captured by the Topcon NW400 camera and detect “more than mild” diabetic retinopathy in adults who have diabetes. The LumineticsCore (formerly known as IDx-DR) falls into the first category of AI disease detectors: Experts have trained the system to look for specific signs of the disease in order to determine whether the disease is present at a predetermined level of severity. If the images are of sufficient quality, the system gives the operator one of two responses: either “More than mild diabetic retinopathy detected: refer to an eye-care professional,” or “Negative for more than mild diabetic retinopathy; retest in 12 months.” Notably, the device doesn’t need a clinician to interpret the image or result, which allows the device to be used by health-care providers who wouldn’t normally be involved in eye care.