IMR Press / RCM / Volume 22 / Issue 4 / DOI: 10.31083/j.rcm2204121
Open Access Review
Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future
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1 Department of Internal Medicine, Dow University of Health Sciences, 74200 Karachi, Pakistan
2 Department of Internal Medicine, Ziauddin University, 75000 Karachi, Pakistan
3 Department of Internal Medicine, Dow Ohja University Hospital, 75330 Karachi, Pakistan
4 Department of Cardiovascular Medicine, University of Louisville, Louisville, KY 40292, USA
5 Institute of Molecular Cardiology, School of Medicine, University of Louisville, Louisville, KY 40292, USA
6 Department of Internal Medicine, Lahore Medical and Dental College, 53400 Lahore, Pakistan
7 Department of Internal Medicine, Staten Island University Hospital, Staten Island, NY 10305, USA
8 Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
*Correspondence: hassanmehmoodlak@gmail.com (Hassan Mehmood Lak)
Academic Editor: Luca Saba
Rev. Cardiovasc. Med. 2021, 22(4), 1095–1113; https://doi.org/10.31083/j.rcm2204121
Submitted: 10 June 2021 | Revised: 16 August 2021 | Accepted: 27 August 2021 | Published: 22 December 2021
(This article belongs to the Special Issue Advanced techniques and future of cardiovascular imaging)
Copyright: © 2021 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Abstract

Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of cardiovascular medicine, and increasingly employed to revolutionize diagnosis, treatment, risk prediction, clinical care, and drug discovery. Heart failure has a high prevalence, and mortality rate following hospitalization being 10.4% at 30-days, 22% at 1-year, and 42.3% at 5-years. Early detection of heart failure is of vital importance in shaping the medical, and surgical interventions specific to HF patients. This has been accomplished with the advent of Neural Network (NN) model, the accuracy of which has proven to be 85%. AI can be of tremendous help in analyzing raw image data from cardiac imaging techniques (such as echocardiography, computed tomography, cardiac MRI amongst others) and electrocardiogram recordings through incorporation of an algorithm. The use of decision trees by Rough Sets (RS), and logistic regression (LR) methods utilized to construct decision-making model to diagnose congestive heart failure, and role of AI in early detection of future mortality and destabilization episodes has played a vital role in optimizing cardiovascular disease outcomes. The review highlights the major achievements of AI in recent years that has radically changed nearly all areas of HF prevention, diagnosis, and management.

Keywords
Deep learning
Decision trees
Heart failure
Artificial neural network
Electronic health records
Echocardiography
Mobile health
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