Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Coronary Artery Disease Detection Using Artificial Intelligence Techniques: A Survey of Trends, Geographical Differences and Diagnostic Features 1991–2020 Publisher Pubmed



Alizadehsani R1 ; Khosravi A1 ; Roshanzamir M2 ; Abdar M1 ; Sarrafzadegan N3, 4 ; Shafie D5 ; Khozeimeh F1 ; Shoeibi A6, 7 ; Nahavandi S1 ; Panahiazar M8 ; Bishara A9 ; Beygui RE10 ; Puri R11 ; Kapadia S11 Show All Authors
Authors
  1. Alizadehsani R1
  2. Khosravi A1
  3. Roshanzamir M2
  4. Abdar M1
  5. Sarrafzadegan N3, 4
  6. Shafie D5
  7. Khozeimeh F1
  8. Shoeibi A6, 7
  9. Nahavandi S1
  10. Panahiazar M8
  11. Bishara A9
  12. Beygui RE10
  13. Puri R11
  14. Kapadia S11
  15. Tan RS12
  16. Acharya UR13, 14, 15
Show Affiliations
Authors Affiliations
  1. 1. Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
  2. 2. Department of Engineering, Fasa Branch, Islamic Azad University, Post Box No 364, Fasa, 7461789818, Fars, Iran
  3. 3. Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Khorram Ave, Isfahan, Iran
  4. 4. Faculty of Medicine, SPPH, University of British Columbia, Vancouver, BC, Canada
  5. 5. Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
  6. 6. Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
  7. 7. Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
  8. 8. Institute for Computational Health Sciences, University of California, San Francisco, United States
  9. 9. Department of Anesthesia and Perioperative Care, University of California, San Francisco, United States
  10. 10. Cardiovascular Surgery Division, Department of Surgery, University of California, San Francisco, CA, United States
  11. 11. Department of Cardiovascular Medicine, Cleveland Clinic, OH, United States
  12. 12. Department of Cardiology, National Heart Centre Singapore, Singapore
  13. 13. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
  14. 14. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
  15. 15. Department of Bioinformatics and Medical Engineering, Asia University, Taiwan

Source: Computers in Biology and Medicine Published:2021


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. © 2020 Elsevier Ltd
Related Docs
View other Related Docs
1. Machine Learning-Based Coronary Artery Disease Diagnosis: A Comprehensive Review, Computers in Biology and Medicine (2019)
2. Ne-Nu-Svc: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease, IEEE Access (2019)
3. Non-Invasive Detection of Coronary Artery Disease in High-Risk Patients Based on the Stenosis Prediction of Separate Coronary Arteries, Computer Methods and Programs in Biomedicine (2018)
4. Coronary Artery Disease Detection Using Computational Intelligence Methods, Knowledge-Based Systems (2016)
5. A Database for Using Machine Learning and Data Mining Techniques for Coronary Artery Disease Diagnosis, Scientific Data (2019)
6. Association Between Work-Related Features and Coronary Artery Disease: A Heterogeneous Hybrid Feature Selection Integrated With Balancing Approach, Pattern Recognition Letters (2020)
7. Diagnosis of Coronary Artery Disease Based on Machine Learning Algorithms Support Vector Machine, Artificial Neural Network, and Random Forest, Advanced Biomedical Research (2023)
8. Application of Data Mining Techniques in Predicting Coronary Heart Disease: A Systematic Review, International Journal of Environmental Health Engineering (2021)
Experts (# of related papers)
View all Related Experts
Nizal Sarrafzadegan (11)
Jamshid Najafian (2)
Other Related Docs
9. Binarized Multi-Gate Mixture of Bayesian Experts for Cardiac Syndrome X Diagnosis: A Clinician-In-The-Loop Scenario With a Belief-Uncertainty Fusion Paradigm, Information Fusion (2023)
10. Hypertrophic Cardiomyopathy Diagnosis Using Deep Learning Techniques, Human-centric Computing and Information Sciences (2024)
11. Survey and Classification of Functional Characteristics in Neural Network Technique for the Diagnosis of Ischemic Heart Disease: A Systematic Review, Journal of Isfahan Medical School (2018)
12. Machine and Deep Learning Algorithms for Classifying Different Types of Dementia: A Literature Review, Applied Neuropsychology:Adult (2024)
13. Predictive Performance of Machine Learning Models for Kidney Complications Following Coronary Interventions: A Systematic Review and Meta-Analysis, International Urology and Nephrology (2025)
14. Diagnostic Performance of Deep Learning Models Versus Radiologists in Covid-19 Pneumonia: A Systematic Review and Meta-Analysis, Clinical Imaging (2024)
15. Fh-Tabnet: Multi-Class Familial Hypercholesterolemia Detection Via a Multi-Stage Tabular Deep Learning Network, European Signal Processing Conference (2024)
16. A New Computer-Aided Diagnosis Tool Based on Deep Learning Methods for Automatic Detection of Retinal Disorders From Oct Images, International Ophthalmology (2024)
17. Deep-Covid: Predicting Covid-19 From Chest X-Ray Images Using Deep Transfer Learning, Medical Image Analysis (2020)
18. Estimation of Burden of Ischemic Heart Diseases in Isfahan, Iran, 2014: Using Incompleteness and Misclassification Adjustment Models, Journal of Diabetes and Metabolic Disorders (2017)
19. Machine Learning-Based Interpretation of Non-Contrast Feature Tracking Strain Analysis and T1/T2 Mapping for Assessing Myocardial Viability, Scientific Reports (2025)
20. Atrial Fibrillation (Af) Detection Using Deep Learning With Gan-Based Data Augmentation, 2023 31st International Conference on Electrical Engineering, ICEE 2023 (2023)
21. Which Risk Factor Best Predicts Coronary Artery Disease Using Artificial Neural Network Method?, BMC Medical Informatics and Decision Making (2024)
22. Explainability, Transparency and Black Box Challenges of Ai in Radiology: Impact on Patient Care in Cardiovascular Radiology, Egyptian Journal of Radiology and Nuclear Medicine (2024)
23. Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition, Journal of Medical Signals and Sensors (2025)
24. An Evolutionary Machine Learning Algorithm for Cardiovascular Disease Risk Prediction, PLoS ONE (2022)