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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2020-04-01
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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 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dong-Ju Choi Jin Joo Park Taqdir Ali Sungyoung Lee |
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Dong-Ju Choi Jin Joo Park Taqdir Ali Sungyoung Lee Artificial intelligence for the diagnosis of heart failure npj Digital Medicine |
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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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