Artificial intelligence on COVID-19 pneumonia detection using chest xray images.

Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of...

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Autores principales:	Lei Rigi Baltazar, Mojhune Gabriel Manzanillo, Joverlyn Gaudillo, Ethel Dominique Viray, Mario Domingo, Beatrice Tiangco, Jason Albia
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9db4264038a545a5a0ffc2389bd64cb7
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spelling	oai:doaj.org-article:9db4264038a545a5a0ffc2389bd64cb72021-12-02T20:07:54ZArtificial intelligence on COVID-19 pneumonia detection using chest xray images.1932-620310.1371/journal.pone.0257884https://doaj.org/article/9db4264038a545a5a0ffc2389bd64cb72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257884https://doaj.org/toc/1932-6203Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.Lei Rigi BaltazarMojhune Gabriel ManzanilloJoverlyn GaudilloEthel Dominique VirayMario DomingoBeatrice TiangcoJason AlbiaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257884 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lei Rigi Baltazar Mojhune Gabriel Manzanillo Joverlyn Gaudillo Ethel Dominique Viray Mario Domingo Beatrice Tiangco Jason Albia Artificial intelligence on COVID-19 pneumonia detection using chest xray images.
description	Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.
format	article
author	Lei Rigi Baltazar Mojhune Gabriel Manzanillo Joverlyn Gaudillo Ethel Dominique Viray Mario Domingo Beatrice Tiangco Jason Albia
author_facet	Lei Rigi Baltazar Mojhune Gabriel Manzanillo Joverlyn Gaudillo Ethel Dominique Viray Mario Domingo Beatrice Tiangco Jason Albia
author_sort	Lei Rigi Baltazar
title	Artificial intelligence on COVID-19 pneumonia detection using chest xray images.
title_short	Artificial intelligence on COVID-19 pneumonia detection using chest xray images.
title_full	Artificial intelligence on COVID-19 pneumonia detection using chest xray images.
title_fullStr	Artificial intelligence on COVID-19 pneumonia detection using chest xray images.
title_full_unstemmed	Artificial intelligence on COVID-19 pneumonia detection using chest xray images.
title_sort	artificial intelligence on covid-19 pneumonia detection using chest xray images.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9db4264038a545a5a0ffc2389bd64cb7
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Artificial intelligence on COVID-19 pneumonia detection using chest xray images.

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