Artificial intelligence for the diagnosis of heart failure

• Published in: NPJ digital medicine Vol. 3; no. 1; p. 54
• Main Authors: Choi, Dong-Ju, Park, Jin Joo, Ali, Taqdir, Lee, Sungyoung
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08-04-2020; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/409; 692/699/75/230; Accuracy; Artificial intelligence; Biomedicine; Biotechnology; Datasets; Digital technology; Dyspnea; Ejection fraction; Health informatics; Heart failure; Medical diagnosis; Medicine; Medicine & Public Health; Heart failure; Outcomes research
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-020-0261-3

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.