The Role of Artificial Intelligence in Early Detection and Risk Stratification of Coronary Artery Disease Using ECG and Imaging Data | [Journal of Pioneering Medical Sciences • 2025]

Author(s):

1. Fahmida Khatoon: Department of Biochemistry, College of Medicine, University of Ha’il, Hail, Saudi Arabia

2. Naglaa Ahmed Abdellatif Ginawi: Accademic Affairs Hail Health Cluster Quality Department, University of Ha’il, Hail, Saudi Arabia

3. Ameera Amirah Fahad Alshammeri: Department of Radiology, College of Medicine, University of Hail, Saudi Arabia

4. Yassir Musa: Department of Radiology, College of Medicine, AlMaarefa University, Kingdom of Saudi Arabia

5. Manal Zayed Alshammary: Pediatric Endocrinologist, College of Medicine, University of Hail, Hail, Saudi Arabia

6. Farida Habib Khan: Department of Family and Community Medicine, College of Medicine, University of Ha’il, Ha’il, Saudi Arabia

7. Sarah Salem Alshammari: College of Medicine, University of Hail, Hail, Saudi Arabia

8. Saud Obaykay Abdulkarim Alshammary: College of Medicine, University of Hail, Hail, Saudi Arabia

9. Khalid Saud Mahal Alnawmasi: College of Medicine, University of Hail, Hail, Saudi Arabia

10. Mohammed Musaad Abdullah Alshammari: College of Medicine, University of Hail, Hail, Saudi Arabia

11. Basmah Mousa Alharbi: College of Medicine, University of Hail, Hail, Saudi Arabia

12. Salma Daher Alshammari: Ministry of Health Headquarters, Internal Audit Unit, Saudi Arabia

13. Hend Faleh Alreshidi: Department of Family Medicine, College of Medicine, University of Ha’il, Hail, Saudi Arabia

Abstract:

Objectives: Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. To evaluate the diagnostic performance of AI models in the early detection and risk stratification of CAD using ECG and imaging data and to compare their accuracy with cardiologist interpretation. Methods: This cross-sectional analytical study was conducted in Saudia, included 185 patients with suspected CAD who underwent both ECG and imaging evaluation. AI-based models, including convolutional neural networks for ECG and deep learning algorithms for imaging, were applied to detect CAD and stratify patients into risk categories. Cardiologist-confirmed diagnosis served as the reference standard. Results: The AI-ECG model achieved a sensitivity of 88.5%, specificity of 82.0% and an AUC of 0.90 (95% CI: 0.86-0.94). When ECG and imaging data were combined, diagnostic accuracy improved, with sensitivity of 92.4%, specificity of 85.2% and an AUC of 0.93 (95% CI: 0.89-0.96). AI-based risk stratification categorized 54 patients (29.2%) as low risk, 78 (42.2%) as intermediate risk and 53 (28.6%) as high risk. Confirmed CAD prevalence correlated strongly with AI-predicted risk groups, with 22.2% in the low-risk group and 88.7% in the high-risk group. Conclusion: Artificial intelligence demonstrates high accuracy in the early detection and risk stratification of CAD using ECG and imaging data. AI models performed comparably to cardiologists and offered significant efficiency gains. Integration of AI into cardiovascular workflows may enable earlier intervention, optimized resource allocation and improved patient outcomes. Further validation across larger and more diverse populations is warranted.

Page(s): 103-107

DOI: 10.47310/jpms2025140913

Published: Journal: Journal of Pioneering Medical Sciences, Volume: 14, Issue: 9, Year: 2025

Keywords:

Coronary artery disease , Risk Stratification , Imaging , Artificial intelligence , Early detection , ECG

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