Open AccessClassification of advanced and early stages of diabetic retinopathy from non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images
Author(s) -
Jennifer Cano,
William D. O’Neill,
Richard D. Penn,
Norman P. Blair,
Amir H Kashani,
Hossein Ameri,
Carolyn Kaloostian,
Mahnaz Shahidi
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.394472
Subject(s) - diabetic retinopathy , ordinary least squares , medicine , optical coherence tomography , ophthalmology , artificial intelligence , diabetes mellitus , optometry , computer science , machine learning , endocrinology
As the prevalence of diabetic retinopathy (DR) continues to rise, there is a need to develop computer-aided screening methods. The current study reports and validates an ordinary least squares (OLS) method to model optical coherence tomography angiography (OCTA) images and derive OLS parameters for classifying proliferative DR (PDR) and no/mild non-proliferative DR (NPDR) from non-diabetic subjects. OLS parameters were correlated with vessel metrics quantified from OCTA images and were used to determine predicted probabilities of PDR, no/mild NPDR, and non-diabetics. The classification rates of PDR and no/mild NPDR from non-diabetic subjects were 94% and 91%, respectively. The method had excellent predictive ability and was validated. With further development, the method may have potential clinical utility and contribute to image-based computer-aided screening and classification of stages of DR and other ocular and systemic diseases.