Zhao YH, Fan YH, Wu XY, Qin T, Sun QT, Liang BH. Determining the scanning range of coronary computed tomography angiography based on deep learning. World J Radiol 2025; 17(7): 110394 [DOI: 10.4329/wjr.v17.i7.110394]
Corresponding Author of This Article
Bao-Hui Liang, School of Medical Imaging, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu 233000, Anhui Province, China. yxwlx@126.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Yu-Hao Zhao, Yi-Han Fan, Tian Qin, Qing-Ting Sun, Bao-Hui Liang, School of Medical Imaging, Bengbu Medical University, Bengbu 233000, Anhui Province, China
Xiao-Yan Wu, School of Mental Health, Bengbu Medical University, Bengbu 233000, Anhui Province, China
Author contributions: Zhao YH conceptualized the study, developed the methodology, and wrote the original draft; Fan YH and Wu XY conducted the investigation and contributed to writing, reviewing and editing; Qin T and Sun QT prepared the visualizations; Fan YH provided resources; Liang BH supervised the study and contributed to writing, reviewing and editing; all authors have read and approved the final manuscript.
Supported by Anhui Provincial College Students’ Innovation and Entrepreneurship Training Program, No. S202310367063.
Institutional review board statement: Ethical approval for this study was obtained from the Ethics Committee of Bengbu Medical University (2024-366).
Informed consent statement: This study was conducted in accordance with the ethical standards of the institutional review board and the Declaration of Helsinki. Given the retrospective and descriptive nature of the study, individual informed consent was not required, as no identifiable patient data were included in the analysis. The institutional review board approved the waiver of informed consent.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Access to data will be provided following a formal request outlining the intended use of the data and subject to ethical approval and data protection regulations.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bao-Hui Liang, School of Medical Imaging, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu 233000, Anhui Province, China. yxwlx@126.com
Received: June 7, 2025 Revised: June 24, 2025 Accepted: July 22, 2025 Published online: July 28, 2025 Processing time: 50 Days and 1.1 Hours
Abstract
BACKGROUND
Coronary computed tomography angiography (CCTA) is essential for diagnosing coronary artery disease as it provides detailed images of the heart’s blood vessels to identify blockages or abnormalities. Traditionally, determining the computed tomography (CT) scanning range has relied on manual methods due to limited automation in this area.
AIM
To develop and evaluate a novel deep learning approach to automate the determination of CCTA scan ranges using anteroposterior scout images.
METHODS
A retrospective analysis was conducted on chest CT data from 1388 patients at the Radiology Department of the First Affiliated Hospital of a university-affiliated hospital, collected between February 27 and March 27, 2024. A deep learning model was trained on anteroposterior scout images with annotations based on CCTA standards. The dataset was split into training (672 cases), validation (167 cases), and test (167 cases) sets to ensure robust model evaluation.
RESULTS
The study demonstrated exceptional performance on the test set, achieving a mean average precision (mAP50) of 0.995 and mAP50-95 of 0.994 for determining CCTA scan ranges.
CONCLUSION
This study demonstrates that: (1) Anteroposterior scout images can effectively estimate CCTA scan ranges; and (2) Estimates can be dynamically adjusted to meet the needs of various medical institutions.
Core Tip: Current coronary computed tomography angiography (CCTA) scanning often requires manual delineation of scan boundaries, limiting automation. This study introduces an innovative deep learning approach to automate CCTA scan range determination using anteroposterior scout images. The method provides a highly precise and adaptable solution, significantly enhancing diagnostic efficiency for coronary artery disease. This advancement overcomes the constraints of manual range selection, facilitating seamless integration across diverse medical institutions and optimizing clinical workflows.
