  Artificial intelligence (AI) classification holds promise as a novel and
affordable screening tool for clinical management of ocular diseases. Rural and
underserved areas, which suffer from lack of access to experienced
ophthalmologists may particularly benefit from this technology. Quantitative
optical coherence tomography angiography (OCTA) imaging provides excellent
capability to identify subtle vascular distortions, which are useful for
classifying retinovascular diseases. However, application of AI for
differentiation and classification of multiple eye diseases is not yet
established. In this study, we demonstrate supervised machine learning based
multi-task OCTA classification. We sought 1) to differentiate normal from
diseased ocular conditions, 2) to differentiate different ocular disease
conditions from each other, and 3) to stage the severity of each ocular
condition. Quantitative OCTA features, including blood vessel tortuosity (BVT),
blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel
density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour
irregularity (FAZ-CI) were fully automatically extracted from the OCTA images.
A stepwise backward elimination approach was employed to identify sensitive
OCTA features and optimal-feature-combinations for the multi-task
classification. For proof-of-concept demonstration, diabetic retinopathy (DR)
and sickle cell retinopathy (SCR) were used to validate the supervised machine
leaning classifier. The presented AI classification methodology is applicable
and can be readily extended to other ocular diseases, holding promise to enable
a mass-screening platform for clinical deployment and telemedicine.
