Abstract
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.
Highlights
Machine learning based artificial intelligence (AI) technology has garnered increasing interest in medical applications over the past few years [1]
Application of AI technology has led to excellent diagnostic accuracy for several ocular conditions such as diabetic retinopathy (DR), age related macular degeneration (AMD), and sickle cell retinopathy (SCR) [2,4,8,9]
We propose to test the feasibility of using these quantitative optical coherence tomography angiography (OCTA) features for machine leaning based multi-task AI screening of different retinopathies
Summary
Machine learning based artificial intelligence (AI) technology has garnered increasing interest in medical applications over the past few years [1]. In the current clinical setting, mass screening programs for common ocular conditions such as DR or SCR are heavily dependent upon experienced physicians to examine and evaluate retinal images. This process is time consuming and expensive, making it difficult to scale up to incorporate the millions of individuals who harbor systematic diseases which are prone to affect the retina. It is not feasible to screen 65 million people in the USA over the age of 50 years [1] to identify for individuals with signs of early retinopathy (AMD, DR or other disease). An AI-based diagnostic tool with capability for multiple-disease differentiation would have tremendous potential to advance mass-level screening of eye diseases [10]
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