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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6617139 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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