Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future

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