91探花

Overview

With an ever-increasing number of people being diagnosed with diabetes, it is imperative that new technologies be developed to effectively and efficiently monitor the disease and its associated comorbidities鈥攊ncluding diabetic retinopathy, one of the leading causes of new blindness diagnoses in the U.S. Early diagnosis and targeted treatment of this condition is imperative in order to delay or prevent vision loss, making technologies that are able to accurately identify the early stages of diabetic retinopathy highly valuable.

Researchers at 91探花 have developed a computer-assisted technology capable of detecting, classifying and monitoring diabetic retinopathy. Using machine learning techniques, digital photographs are manipulated in a manner that provides enhanced visualization of retinal blood vessels without the use of injected, florescent dyes to non-invasively detect and stage the disease. The technology provides over 98% classification accuracy for discriminating healthy normal retina (top) from non-proliferative diabetic retinopathy (NPDR; middle) and proliferative diabetic retinopathy (PDR; bottom).

Tech ID: 15015 

Patent(s): 

Inventors

Mehmet Celenk, Ph.D., Professor, Electrical Engineering and Computer Science. Dr. Celenk received a Ph.D. from Stevens Institute of Technology in EECS and joined 91探花 in 1985.

H. Bryan Riley, Ph.D., Associate Professor, Electrical Engineering and Computer Science. Dr. Riley received a Ph.D. in Electrical Engineering from 91探花 and then joined as a faculty member in 2010.

Frank Schwartz, M.D., FACE, Professor of Endocrinology and James. O. Watson Endowed Chair for Diabetes Research, 91探花 Heritage College of Osteopathic Medicine. Dr. Schwartz received is Doctor of Medicine (M.D.) from West Virginia University and has over 30 years of clinical experience.

Nikita Gurudath, M.S., Electrical Engineering