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.
Highlights
The prevalence of heart failure (HF) has been increasing[1,2]
Correct diagnosis of HF can be challenging for physicians, even for HF specialists
In a prospective cohort of patients presenting with dyspnea to the outpatient clinic, Artificial Intelligence-Clinical Decision Support System (AI-Clinical Decision Support System (CDSS)) consistently showed a remarkably high diagnostic accuracy
Summary
The prevalence of heart failure (HF) has been increasing[1,2]. HF is associated with high morbidity and mortality[3]. Because HF is a complex syndrome that can result from structural and functional cardiac disorder, rather than a single disease entity, its correct diagnosis can be challenging even for HF specialists. HF is classified according to ejection fraction, i.e., HF with reduced ejection fraction (HFrEF), HF with mid-range ejection fraction (HFmrEF), and HF with preserved ejection fraction (HFpEF)[4]. A correct diagnosis is mandatory before proper treatment can be initiated[4,5]. Present-day physicians are challenged by rapidly changing scientific evidences, new drugs, and the complexity of guidelines for HF management, especially in outpatient clinic. With enormous advancements in information and communication technologies, such as easy storage, acquisition, and recovery of big data and knowledge, artificial intelligence (AI) has been gaining an important role in cardiology[6]
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