HOUSTON, Aug. 6, 2020 /PRNewswire/ -- RETINA-AI Health, Inc. raised $5.2M in Series A financing for AI screening of diabetic retinopathy.
The capital was raised from private investors, 80% of whom are physicians. Also included amongst the investors are business titans such as Bill Smith, Founder of Shipt (acquired by Target in 2018).
Diabetes affects up to 35 million Americans, and each person with diabetes needs at least one retinal exam per year. However, due to a number of factors including cost, inconvenience, lack of transportation, and a shortage of eye specialists, more than half of people with diabetes in the U.S. do not get their annual retinal exam. And this too often results in preventable blindness.
RETINA-AI Health, Inc. has developed artificial intelligence technology to enable the diabetic retinal exam to be done in the primary care setting. The company's HUMMINGBIRD DR 100™ is a cloud-based AI detector of diabetic retinopathy which interprets the retinal image and returns a PDF report within a few seconds.
RETINA-AI Health Inc.'s founder and CEO, Dr. Stephen Odaibo, a retina specialist, computer scientist, and full-stack AI engineer says the funds will be used to take the company's retina-based AI detection technology through the FDA.
Dr. Richard Y. Hwang, MD, PhD, a retina specialist in El Paso, Texas, says, "artificial intelligence has the potential to make a significant impact in diabetic retinopathy screening by improving the efficiency, cost-effectiveness, and the accessibility of screening programs."
Dr. Odaibo adds that "the confluence of value-based healthcare delivery and artificial intelligence provides a tangible and timely opportunity for positive impact."
ABOUT RETINA-AI Health, Inc. RETINA-AI Health, Inc. is a privately-held Delaware C-Corp founded in 2017 and headquartered in Houston Texas. The company is focused on building artificial intelligence to improve healthcare outcomes of prevalent diseases such as diabetes. More broadly, the company develops and deploys retina-based AI for detection of disease at scale.