1 in every 200 people with eye-related irritation in the US has uveitis. Vision loss can occur if the uveitis remains untreated, therefore, early detection is crucial.

Certain uveitis cases currently require fluorescein angiography (FA) for diagnosis due to its ability to display retinal vascular leakage (RVL). However, FA is invasive and side effects have been reported. Additionally, the procedure is time-consuming and imposes economic burdens on patients, physicians, and payors.

NIH inventors have developed a deep learning tool to non-invasively detect RVL using ultrawide-field color fundus photos. This algorithm identifies fundus images with and without RVL with high accuracy (79%) and sensitivity (85%). Compared to the current gold standard of assessing RVL (clinician interpretation), this deep learning tool provides an improved method of detecting RVL for patients with uveitis.

Competitive advantages of this tool are:

• Greater accuracy and sensitivity versus the current gold standard to assess RVL (clinician assessment)
• Deep learning tool to assess ultrawide-field color fundus images and assess RVL

If you are interested in licensing or collaborating on this diagnostic tool to predict uveitis, you can find more information on the abstract: Using Artificial Intelligence To Diagnose Uveitis