SBIR/STTR Award attributes
PROJECT SUMMARY The CDC recommends that each of the 34.2 million patients with diabetes in the United States is screened annually for diabetic retinopathy (DR), a major cause of preventable blindness. Less than 50% of diabetes patients actually follow these guidelines due to lack of access to medical care and eye specialists, time and money constraints, and lack of symptoms with early-stage disease. To address these problems, our team at AI Optics is developing the world’s first artificial intelligence-based handheld retinal camera to allow for point-of-care DR screening. This device is designed to be portable, easy to use, and workflow friendly. It performs high-accuracy DR screenings on non-dilated patients, maintaining optimal security and remaining resilient to connectivity issues. Our goal is that this novel diagnostic device will expand DR screenings beyond the offices of eye specialists and into primary care, optometry offices, diabetes clinics, and retail health settings. This increased access to screening will increase early-stage diagnosis rates and avoid preventable vision loss. In this Phase I SBIR project, we will develop a retinal camera that complies with ISO 10940:2009 standards, which will be able to capture high-quality fundus images in a handheld device. To ensure that full-scale image classification can be conducted, we will also utilize our proprietary, deep-learning artificial intelligence system. Finally, we will ensure that images captured from our retinal camera can be analyzed by our artificial intelligence for the presence of DR. The successful completion of this project will result in an improved and more accessible tool for DR screenings that could lead to earlier DR diagnosis, blindness prevention, and significant cost savings for millions of people with diabetes.PROJECT NARRATIVE Less than 50% of the 34.2 million people with diabetes in the United States follow CDC guidelines to get annual screenings for diabetic retinopathy (DR), a leading cause of blindness that impacts up to 40% of diabetic patients. To address this problem, we propose the development of the world’s first handheld retinal camera that uses artificial intelligence for point-of-care DR screenings in primary care, urgent care, and retail health settings. As it can be operated without an eye specialist or Internet access, this handheld device will be portable, easy to use, and workflow friendly, enabling more screenings, earlier DR diagnosis, vision-loss prevention and significant cost savings for millions.
