Applications of Artificial Intelligence in Coronary Computed Tomography Angiography: Progress and Challenges

Xinhong Wang; Zhen Wang; Xincheng Li; Haipeng Liu

doi:10.1201/9781003481621-12

Applications of Artificial Intelligence in Coronary Computed Tomography Angiography: Progress and Challenges

Xinhong Wang
, Zhen Wang
, Xincheng Li
, Haipeng Liu

The Second Affiliated Hospital Zhejiang University
Zhejiang University School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

CCTA (coronary computed tomography angiography) is an important tool for evaluating patients with suspected stable coronary artery disease. Recently, the development of artificial intelligence (AI), including machine learning in data analytics and deep learning in image processing, is reshaping the landscape of CCTA in clinical practice. The noise, radiation dose, and motion artifacts have been largely reduced. More advanced algorithms have been proposed for geometric construction, including image segmentation and centerline extraction. Based on the improved image quality, the assessment of different components (calcification, plaque, stenosis, myocardium, and pericardial fat) has achieved higher accuracy. Computational simulation can estimate hemodynamic parameters like fractional flow reserve. These new applications enable clinicians to improve the accuracy of diagnosis and treatment of coronary artery disease. This chapter summarizes the state-of-the-art methods of AI in CCTA, providing an updated reference for biomedical engineers, health professionals, and policymakers.

Original language	English
Title of host publication	Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases
Subtitle of host publication	Towards Data-Driven Smart Healthcare
Editors	Haipeng Liu, Gary Tse
Publisher	CRC Press, Taylor & Francis Group
Chapter	12
Pages	220-231
Number of pages	12
Edition	1
ISBN (Electronic)	9781003481621
ISBN (Print)	9781032771694
DOIs	https://doi.org/10.1201/9781003481621-12
Publication status	Published - 23 Jun 2025

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

Coronary computed tomography angiography
artificial intelligence
machine learning

Access to Document

10.1201/9781003481621-12

Cite this

Wang, X., Wang, Z., Li, X., & Liu, H. (2025). Applications of Artificial Intelligence in Coronary Computed Tomography Angiography: Progress and Challenges. In H. Liu, & G. Tse (Eds.), Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare (1 ed., pp. 220-231). CRC Press, Taylor & Francis Group. https://doi.org/10.1201/9781003481621-12

Applications of Artificial Intelligence in Coronary Computed Tomography Angiography: Progress and Challenges. / Wang, Xinhong; Wang, Zhen; Li, Xincheng et al.
Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare. ed. / Haipeng Liu; Gary Tse. 1. ed. CRC Press, Taylor & Francis Group, 2025. p. 220-231.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Wang, X, Wang, Z, Li, X & Liu, H 2025, Applications of Artificial Intelligence in Coronary Computed Tomography Angiography: Progress and Challenges. in H Liu & G Tse (eds), Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare. 1 edn, CRC Press, Taylor & Francis Group, pp. 220-231. https://doi.org/10.1201/9781003481621-12

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abstract = "CCTA (coronary computed tomography angiography) is an important tool for evaluating patients with suspected stable coronary artery disease. Recently, the development of artificial intelligence (AI), including machine learning in data analytics and deep learning in image processing, is reshaping the landscape of CCTA in clinical practice. The noise, radiation dose, and motion artifacts have been largely reduced. More advanced algorithms have been proposed for geometric construction, including image segmentation and centerline extraction. Based on the improved image quality, the assessment of different components (calcification, plaque, stenosis, myocardium, and pericardial fat) has achieved higher accuracy. Computational simulation can estimate hemodynamic parameters like fractional flow reserve. These new applications enable clinicians to improve the accuracy of diagnosis and treatment of coronary artery disease. This chapter summarizes the state-of-the-art methods of AI in CCTA, providing an updated reference for biomedical engineers, health professionals, and policymakers.",

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N1 - Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

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AB - CCTA (coronary computed tomography angiography) is an important tool for evaluating patients with suspected stable coronary artery disease. Recently, the development of artificial intelligence (AI), including machine learning in data analytics and deep learning in image processing, is reshaping the landscape of CCTA in clinical practice. The noise, radiation dose, and motion artifacts have been largely reduced. More advanced algorithms have been proposed for geometric construction, including image segmentation and centerline extraction. Based on the improved image quality, the assessment of different components (calcification, plaque, stenosis, myocardium, and pericardial fat) has achieved higher accuracy. Computational simulation can estimate hemodynamic parameters like fractional flow reserve. These new applications enable clinicians to improve the accuracy of diagnosis and treatment of coronary artery disease. This chapter summarizes the state-of-the-art methods of AI in CCTA, providing an updated reference for biomedical engineers, health professionals, and policymakers.

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Applications of Artificial Intelligence in Coronary Computed Tomography Angiography: Progress and Challenges

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