VisionQuest Uses Artificial Intelligence to Screen 40,000 Patients for Diabetic Retinopathy

ALBUQUERQUE, New Mexico--(BUSINESS WIRE)--VisionQuest Biomedical Inc. announces that the company has used the EyeStar artificial intelligence (AI) software to screen over 40,000 patients for diabetic retinopathy at the Clínicas del Azúcar.

The EyeStar AI system screens patients for diabetic retinopathy and macular edema, conditions that lead to vision loss and blindness if left untreated. VisionQuest brought the EyeStar AI system to Clínicas del Azúcar in 2016, and since then the software has identified over 4,000 patients with sight-threatening disease who would not otherwise have been referred for care by a specialist in a timely manner.

Javier Lozano, CEO of Clínicas del Azúcar, pioneered the “one-stop shop” concept of diabetes care. In 2011 he founded Clínicas del Azúcar in Monterrey, Mexico, and since then has expanded to twenty one clinics in eight cities throughout the country. At the clinics, Lozano and VisionQuest have established innovative technologies, including AI, to serve both patients and providers, bringing health care to the people who need it most in efficient and cost-effective ways.

The screening procedure at Clínicas del Azúcar is straightforward and quick: A patient arrives at the clinic and has their vital measurements recorded. A nurse or technician takes retinal photographs (without needing to dilate the patient’s pupils) using a tabletop or handheld camera and uploads them to the cloud, where the user-friendly EyeStar system applies a deep-learning classifier to determine whether the patient needs to be referred to an ophthalmologist. EyeStar’s analysis takes less than a minute and allows physicians at the clinics to manage their patients with efficiency and care and to refer them to a specialist when needed.

“We have demonstrated that EyeStar fits the clinical needs of diabetes clinics, with high accuracy of disease detection and seamless integration with the clinics’ workflow,” says VisionQuest CEO Simon Barriga. “Our system allows the clinics to provide diabetic eye-disease screening to 100 percent of their patients in a country where compliance with annual diabetic eye exams is less than 20 percent,” he continued.

VisionQuest’s quality assurance program shows that EyeStar can provide a result in 98 percent of cases due to the software’s resilience to variations in image quality. EyeStar was developed with funding from the National Eye Institute. The system uses state-of-the-art deep-learning algorithms trained on hundreds of thousands of images from VisionQuest’s proprietary dataset to achieve over 90 percent sensitivity in the detection of diabetic retinopathy. Providers refer only patients who are found to have severe disease for dilated-eye exams and possible treatment, which makes the best use of scarce eye-care resources in Mexico. EyeStar thus has a major positive impact on clinical efficiency and health-care access, as well as improving outcomes for patients who might otherwise be reluctant to return for further care.

About VisionQuest Biomedical Inc.: VisionQuest develops and delivers innovative artificial intelligence–based imaging technologies that increase access to health care for the people who need it the most. We serve patients and providers in the most efficient and cost-effective ways possible. Dr. Peter Soliz founded VisionQuest in 2007 to develop AI techniques that could be used by health-care professionals to evaluate digital medical photographs, specifically retinal images that showed evidence of diabetic retinopathy—the most common complication of diabetes and the leading cause of blindness in the working-age population—and other pathologies. In the United States, VisionQuest has established a network of clinics in which to study computer-based detection of retinal pathologies. In Mexico, our EyeStar software is used to screen patients for diabetic retinopathy. In the sub-Saharan country of Malawi, VisionQuest is applying retinal screening to the detection of malarial retinopathy.