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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Main Authors: Dong-Ju Choi, Jin Joo Park, Taqdir Ali, Sungyoung Lee
Format: Article
Language:English
Published: Nature Publishing Group 2020-04-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-020-0261-3
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spelling doaj-a27030dcc8fa420c879eb3148fa4b5ec2021-04-11T11:37:35ZengNature Publishing Groupnpj Digital Medicine2398-63522020-04-01311610.1038/s41746-020-0261-3Artificial intelligence for the diagnosis of heart failureDong-Ju Choi0Jin Joo Park1Taqdir Ali2Sungyoung Lee3Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang HospitalDivision of Cardiology, Department of Internal Medicine, Seoul National University Bundang HospitalDepartment of Computer Science and Engineering, Kyung Hee UniversityDepartment of Computer Science and Engineering, Kyung Hee UniversityAbstract 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.https://doi.org/10.1038/s41746-020-0261-3
collection DOAJ
language English
format Article
sources DOAJ
author Dong-Ju Choi
Jin Joo Park
Taqdir Ali
Sungyoung Lee
spellingShingle Dong-Ju Choi
Jin Joo Park
Taqdir Ali
Sungyoung Lee
Artificial intelligence for the diagnosis of heart failure
npj Digital Medicine
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 Publishing Group
series npj Digital Medicine
issn 2398-6352
publishDate 2020-04-01
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.
url https://doi.org/10.1038/s41746-020-0261-3
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