Volume 16, Issue 6 And S7 (Artificial Intelligence- In Press 2026)
J Research Health 2026, 16(6 And S7): 7-7 |
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Monjezi R, Ghasemi M, Rowhanimanesh A, Tabaee S S. Artificial Intelligence in Coronary Angiography: A Systematic Review of Automated Detection, Segmentation, 3D Reconstruction, and Risk Prediction. , J Research Health. 2026; 16 (6 and S7) :7-7
URL: http://jrh.gmu.ac.ir/article-1-3007-en.html
URL: http://jrh.gmu.ac.ir/article-1-3007-en.html
1- Department of Intelligent & Cognitive Technologies, Faculty of Novel Interdisciplinary Technologies, University of Neyshabur, Iran.
2- Department of Intelligent & Cognitive Technologies, Faculty of Novel Interdisciplinary Technologies, University of Neyshabur, Iran. & Research Institute for Novel Medical Technologies, University of Neyshabur, Iran. ,ghasemi.biomed@gmail.com
3- Department of Intelligent & Cognitive Technologies, Faculty of Novel Interdisciplinary Technologies, University of Neyshabur, Iran. & Research Institute for Novel Medical Technologies, University of Neyshabur, Iran.
4- Noncommunicable Disease Research Center, Neyshabur University of Medical Sciences, Iran.
2- Department of Intelligent & Cognitive Technologies, Faculty of Novel Interdisciplinary Technologies, University of Neyshabur, Iran. & Research Institute for Novel Medical Technologies, University of Neyshabur, Iran. ,
3- Department of Intelligent & Cognitive Technologies, Faculty of Novel Interdisciplinary Technologies, University of Neyshabur, Iran. & Research Institute for Novel Medical Technologies, University of Neyshabur, Iran.
4- Noncommunicable Disease Research Center, Neyshabur University of Medical Sciences, Iran.
Abstract: (22 Views)
Background: This review analyzes AI's transformative role across the full spectrum of coronary imaging (CCTA[1] and XCA[2]). AI is addressing critical limitations by enhancing image quality, enabling personalized interpretation, and improving workflow efficiency.
Methods: In this study, we conducted a comprehensive search of the scientific literature for studies on the application of AI in CCTA or XCA to date. Searches were performed in PubMed/MEDLINE, Embase, and IEEE Xplore. Two reviewers then independently screened the selected articles for data extraction, which a third reviewer validated. The findings were ultimately organized into five topic areas: the role of AI in coronary CTA, coronary artery segmentation, automated coronary artery detection and classification, 3D image reconstruction of coronary arteries, and risk prediction assessment.
Results: From the 98 records identified, 62 studies met the inclusion criteria. In CCTA, AI applications extend from anatomical assessment to automated plaque quantification, functional assessment via FFR[3]-CT, and the extraction of novel biomarkers. For XCA, AI facilitates automated stenosis quantification, 3D reconstruction, and real-time guidance. Key innovations include advanced architectures like DR-LCT-U-Net, benchmark datasets such as CADICA, and specialized systems like CAG-VLM for diagnostic support. Ultimately, AI models significantly outperform conventional methods in risk stratification.
Conclusion: While AI offers significant potential to bridge CCTA and XCA, its clinical implementation faces hurdles in interpretability, integration, and validation. Realizing personalized coronary artery disease management will require prospective trials and explainable AI frameworks.
Methods: In this study, we conducted a comprehensive search of the scientific literature for studies on the application of AI in CCTA or XCA to date. Searches were performed in PubMed/MEDLINE, Embase, and IEEE Xplore. Two reviewers then independently screened the selected articles for data extraction, which a third reviewer validated. The findings were ultimately organized into five topic areas: the role of AI in coronary CTA, coronary artery segmentation, automated coronary artery detection and classification, 3D image reconstruction of coronary arteries, and risk prediction assessment.
Results: From the 98 records identified, 62 studies met the inclusion criteria. In CCTA, AI applications extend from anatomical assessment to automated plaque quantification, functional assessment via FFR[3]-CT, and the extraction of novel biomarkers. For XCA, AI facilitates automated stenosis quantification, 3D reconstruction, and real-time guidance. Key innovations include advanced architectures like DR-LCT-U-Net, benchmark datasets such as CADICA, and specialized systems like CAG-VLM for diagnostic support. Ultimately, AI models significantly outperform conventional methods in risk stratification.
Conclusion: While AI offers significant potential to bridge CCTA and XCA, its clinical implementation faces hurdles in interpretability, integration, and validation. Realizing personalized coronary artery disease management will require prospective trials and explainable AI frameworks.
Keywords: Coronary Angiography, Artificial Intelligence, Automated Detection, Segmentation, 3D Reconstruction, Risk Prediction
Type of Study: Review Article |
Subject:
● Artificial Intelligence
Received: 2025/12/11 | Accepted: 2026/10/13 | Published: 2026/11/11
Received: 2025/12/11 | Accepted: 2026/10/13 | Published: 2026/11/11
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