SYSTEMATIC REVIEW article
Front. Cardiovasc. Med.
Sec. Cardiovascular Imaging
Application of Artificial Intelligence in Non-Invasive Cardiovascular Imaging for Coronary Artery Disease: A Systematic Review and Meta-analysis
Provisionally accepted
- 1Bayer Healthcare Co. Ltd., Shanghai, China
- 2Bayer AG, Olso, Norway
- 3Bayer Medical Care Inc, Indianola, United States
- 4Bayer Healthcare Company, Beijing, China
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ABSTRACT Introduction: Coronary artery disease (CAD) remains a leading cause of death worldwide. While non-invasive imaging techniques are widely used for diagnosis, their interpretation can be time-consuming and subject to intra-and inter-observer variability. Artificial intelligence (AI), including machine learning and deep learning, offers potential advantages in improving diagnostic accuracy and efficiency by rapidly processing large imaging datasets. Methods: A systematic review was conducted to evaluate current evidence on AI applications in non-invasive CAD imaging. Searches were performed in PubMed, Embase, Web of Science, Engineering Index, and the Cochrane Library for studies published between 2018 and 2023. A total of 122 studies were included in the evidence map, and 9 studies assessing AI for detecting ≥50% coronary stenosis were selected for meta-analysis. Results: The pooled sensitivity and specificity for detecting stenosis were 0.94 and 0.69, respectively, at the patient level, and 0.81 and 0.88 at the vessel level. The area under the SROC curve was 0.83 (patient level) and 0.92 (vessel level), indicating good diagnostic performance. High heterogeneity was observed across studies. Discussion: These findings suggest that AI holds promise for enhancing the diagnostic process in CAD imaging. However, variability in methodologies and AI implementation underscores the need for standardization and further prospective validation.
Keywords: artificial intelligence, Non-invasive imaging modalities, cardiovascular imaging, Coronary Artery Disease, Systematic review
Received: 11 Jul 2025; Accepted: 27 Nov 2025.
Copyright: © 2025 Liu, Reis, Sharma and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Baiyun Liu
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