Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020
Article
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 R., Tan, Ru-San and Acharya, U. Rajendra. 2021. "Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020." Computers in Biology and Medicine. 128. https://doi.org/10.1016/j.compbiomed.2020.104095
Article Title | Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020 |
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ERA Journal ID | 5040 |
Article Category | Article |
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 R., Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 128 |
Article Number | 104095 |
Number of Pages | 16 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2020.104095 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482520304261 |
Abstract | 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. |
Keywords | Accuracy |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Deakin University |
Islamic Azad University, Iran | |
Isfahan University of Medical Sciences, Iran | |
Ferdowsi University of Mashhad, Iran | |
University of California, United States | |
Cleveland Clinic, United States | |
National Heart Centre, Singapore | |
School of Mathematics, Physics and Computing | |
Institute for Life Sciences and the Environment | |
Centre for Health Research |
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