Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies.

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

SUBMITTER: Alam M 

PROVIDER: S-EPMC6617139 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature