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

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 opt...

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Detalles Bibliográficos
Autores principales: Alam, Minhaj, Le, David, Lim, Jennifer I., Chan, Robison V.P., Yao, Xincheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617139/
https://www.ncbi.nlm.nih.gov/pubmed/31216768
http://dx.doi.org/10.3390/jcm8060872
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author Alam, Minhaj
Le, David
Lim, Jennifer I.
Chan, Robison V.P.
Yao, Xincheng
author_facet Alam, Minhaj
Le, David
Lim, Jennifer I.
Chan, Robison V.P.
Yao, Xincheng
author_sort Alam, Minhaj
collection PubMed
description 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.
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spelling pubmed-66171392019-07-18 Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies Alam, Minhaj Le, David Lim, Jennifer I. Chan, Robison V.P. Yao, Xincheng J Clin Med Article 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. MDPI 2019-06-18 /pmc/articles/PMC6617139/ /pubmed/31216768 http://dx.doi.org/10.3390/jcm8060872 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alam, Minhaj
Le, David
Lim, Jennifer I.
Chan, Robison V.P.
Yao, Xincheng
Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies
title Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies
title_full Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies
title_fullStr Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies
title_full_unstemmed Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies
title_short Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies
title_sort supervised machine learning based multi-task artificial intelligence classification of retinopathies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617139/
https://www.ncbi.nlm.nih.gov/pubmed/31216768
http://dx.doi.org/10.3390/jcm8060872
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