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Open AccessArticle

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

1
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
2
Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(6), 872; https://doi.org/10.3390/jcm8060872
Received: 24 May 2019 / Revised: 11 June 2019 / Accepted: 12 June 2019 / Published: 18 June 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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. View Full-Text
Keywords: ophthalmology; diabetic retinopathy; sickle cell retinopathy; quantitative analysis; computer aided diagnosis; artificial intelligence; support vector machine; optical coherence tomography angiography ophthalmology; diabetic retinopathy; sickle cell retinopathy; quantitative analysis; computer aided diagnosis; artificial intelligence; support vector machine; optical coherence tomography angiography
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MDPI and ACS Style

Alam, M.; Le, D.; Lim, J.I.; Chan, R.V.P.; Yao, X. Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies. J. Clin. Med. 2019, 8, 872. https://doi.org/10.3390/jcm8060872

AMA Style

Alam M, Le D, Lim JI, Chan RVP, Yao X. Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies. Journal of Clinical Medicine. 2019; 8(6):872. https://doi.org/10.3390/jcm8060872

Chicago/Turabian Style

Alam, Minhaj; Le, David; Lim, Jennifer I.; Chan, Robison V.P.; Yao, Xincheng. 2019. "Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies" J. Clin. Med. 8, no. 6: 872. https://doi.org/10.3390/jcm8060872

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