Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020

• Published in: Computers in biology and medicine Vol. 128; p. 104095
• Main Authors: Alizadehsani, Roohallah, Khosravi, Abbas, Roshanzamir, Mohamad, Abdar, Moloud, Sarrafzadegan, Nizal, Shafie, Davood, Khozeimeh, Fahime, Shoeibi, Afshin, Nahavandi, Saeid, Panahiazar, Maryam, Bishara, Andrew, Beygui, Ramin E., Puri, Rishi, Kapadia, Samir, Tan, Ru-San, Acharya, U Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2021; Elsevier Limited
• Subjects: Accuracy; Angiography; Artificial intelligence; Cardiovascular disease; Classification; Coronary artery; Coronary artery disease; Datasets; Diagnosis; Diagnostic software; Diagnostic systems; ECG; EKG; Electrocardiography; Features; Heart diseases; Heart rate; Internal Medicine; Learning algorithms; Machine learning; Medical diagnosis; Medical imaging; Other; Search engines; Signs and symptoms; Trends; ECG; Accuracy; Features; Artificial intelligence; Coronary artery disease; Classification
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.104095

While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption. •This research gives a comprehensive insight to the researchers of this field.•The researchers can find the algorithms or feature categories that have been less investigated.•It shows that in which countries this field was more interested for researchers.•The importance of features according to the published papers is also reported in this research.•It shows the importance of different features in various countries.