Artificial intelligence for the diagnosis of heart failure

Abstract The diagnosis of heart failure can be difficult, even for heart failure specialists. Artificial Intelligence-Clinical Decision Support System (AI-CDSS) has the potential to assist physicians in heart failure diagnosis. The aim of this work was to evaluate the diagnostic accuracy of an AI-CD...

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Autores principales:	Dong-Ju Choi, Jin Joo Park, Taqdir Ali, Sungyoung Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a27030dcc8fa420c879eb3148fa4b5ec
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spelling	oai:doaj.org-article:a27030dcc8fa420c879eb3148fa4b5ec2021-12-02T14:26:07ZArtificial intelligence for the diagnosis of heart failure10.1038/s41746-020-0261-32398-6352https://doaj.org/article/a27030dcc8fa420c879eb3148fa4b5ec2020-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0261-3https://doaj.org/toc/2398-6352Abstract The diagnosis of heart failure can be difficult, even for heart failure specialists. Artificial Intelligence-Clinical Decision Support System (AI-CDSS) has the potential to assist physicians in heart failure diagnosis. The aim of this work was to evaluate the diagnostic accuracy of an AI-CDSS for heart failure. AI-CDSS for cardiology was developed with a hybrid (expert-driven and machine-learning-driven) approach of knowledge acquisition to evolve the knowledge base with heart failure diagnosis. A retrospective cohort of 1198 patients with and without heart failure was used for the development of AI-CDSS (training dataset, n = 600) and to test the performance (test dataset, n = 598). A prospective clinical pilot study of 97 patients with dyspnea was used to assess the diagnostic accuracy of AI-CDSS compared with that of non-heart failure specialists. The concordance rate between AI-CDSS and heart failure specialists was evaluated. In retrospective cohort, the concordance rate was 98.3% in the test dataset. The concordance rate for patients with heart failure with reduced ejection fraction, heart failure with mid-range ejection fraction, heart failure with preserved ejection fraction, and no heart failure was 100%, 100%, 99.6%, and 91.7%, respectively. In a prospective pilot study of 97 patients presenting with dyspnea to the outpatient clinic, 44% had heart failure. The concordance rate between AI-CDSS and heart failure specialists was 98%, whereas that between non-heart failure specialists and heart failure specialists was 76%. In conclusion, AI-CDSS showed a high diagnostic accuracy for heart failure. Therefore, AI-CDSS may be useful for the diagnosis of heart failure, especially when heart failure specialists are not available.Dong-Ju ChoiJin Joo ParkTaqdir AliSungyoung LeeNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-6 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Dong-Ju Choi Jin Joo Park Taqdir Ali Sungyoung Lee Artificial intelligence for the diagnosis of heart failure
description	Abstract The diagnosis of heart failure can be difficult, even for heart failure specialists. Artificial Intelligence-Clinical Decision Support System (AI-CDSS) has the potential to assist physicians in heart failure diagnosis. The aim of this work was to evaluate the diagnostic accuracy of an AI-CDSS for heart failure. AI-CDSS for cardiology was developed with a hybrid (expert-driven and machine-learning-driven) approach of knowledge acquisition to evolve the knowledge base with heart failure diagnosis. A retrospective cohort of 1198 patients with and without heart failure was used for the development of AI-CDSS (training dataset, n = 600) and to test the performance (test dataset, n = 598). A prospective clinical pilot study of 97 patients with dyspnea was used to assess the diagnostic accuracy of AI-CDSS compared with that of non-heart failure specialists. The concordance rate between AI-CDSS and heart failure specialists was evaluated. In retrospective cohort, the concordance rate was 98.3% in the test dataset. The concordance rate for patients with heart failure with reduced ejection fraction, heart failure with mid-range ejection fraction, heart failure with preserved ejection fraction, and no heart failure was 100%, 100%, 99.6%, and 91.7%, respectively. In a prospective pilot study of 97 patients presenting with dyspnea to the outpatient clinic, 44% had heart failure. The concordance rate between AI-CDSS and heart failure specialists was 98%, whereas that between non-heart failure specialists and heart failure specialists was 76%. In conclusion, AI-CDSS showed a high diagnostic accuracy for heart failure. Therefore, AI-CDSS may be useful for the diagnosis of heart failure, especially when heart failure specialists are not available.
format	article
author	Dong-Ju Choi Jin Joo Park Taqdir Ali Sungyoung Lee
author_facet	Dong-Ju Choi Jin Joo Park Taqdir Ali Sungyoung Lee
author_sort	Dong-Ju Choi
title	Artificial intelligence for the diagnosis of heart failure
title_short	Artificial intelligence for the diagnosis of heart failure
title_full	Artificial intelligence for the diagnosis of heart failure
title_fullStr	Artificial intelligence for the diagnosis of heart failure
title_full_unstemmed	Artificial intelligence for the diagnosis of heart failure
title_sort	artificial intelligence for the diagnosis of heart failure
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a27030dcc8fa420c879eb3148fa4b5ec
work_keys_str_mv	AT dongjuchoi artificialintelligenceforthediagnosisofheartfailure AT jinjoopark artificialintelligenceforthediagnosisofheartfailure AT taqdirali artificialintelligenceforthediagnosisofheartfailure AT sungyounglee artificialintelligenceforthediagnosisofheartfailure
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Artificial intelligence for the diagnosis of heart failure

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