ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Cardiovascular Imaging
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1647882
This article is part of the Research TopicAdvances in Medical Imaging and Artificial Intelligence: Diagnosis and TreatmentView all articles
Deep Learning-Based Automated Quantification System for Abdominal Aortic Calcification: Multicenter Cohort Study for Algorithm Development and Clinical Validation
Provisionally accepted
- 1Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo, China
- 2Department of Research and Development, United Imaging Intelligence, Shanghai, China
- 3Department of Radiology, Cixi City People’s Hospital, Ningbo, China
- 4Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
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Objectives: To establish an automated scoring system for abdominal aortic calcification (AAC) to facilitate standardized quantitative imaging analysis in support of clinical decision-making in atherosclerosis management. Methods: X-ray images of the abdominal aorta were obtained for 2,941 individuals from five medical centers in Zhejiang Province. Calcification severity was graded manually using the Kauppila scoring system, and cases were stratified into three groups based on total calcification burden. The automated assessment framework comprised two sequential components: a lumbar spine segmentation model based on nnUnet and an AAC score regression model based on ResNet. Model development was conducted using 1,737 training cases, with internal validation in 471 cases and external validation in 733 cases from independent centers. A retrospective matched cohort study was conducted in 200 AAC patients from Center B (100 dialysis-dependent and 100 not dialysis-dependent cases), to investigate associations with major adverse cardiovascular events. Results: The developed automated quantification system demonstrated mean absolute errors of 1.686 (internal validation set) and 1.920 (external validation set), with strong correlation to expert ratings (Spearman’s ρ=0.923 and 0.888, respectively, both P<0.001). Inter-rater reliability analysis revealed excellent agreement with manual scoring (intraclass correlation coefficients of 0.913 internally and 0.874 externally). Stratification based on calcification severity showed optimal sensitivity for the moderate calcification category (88.6%), with superior specificity for the non/mild (94.2%) and severe (91.5%) categories. Conclusion: The established automated quantification system for AAC exhibits good assessment efficiency and measurement accuracy, offering a standardized approach to refine cardiovascular risk stratification in clinical practice.
Keywords: deep learning, ABDOMINAL AORTIC CALCIFICATION, cardiovascular riskstratification, Automated quantification, X-ray image
Received: 16 Jun 2025; Accepted: 03 Oct 2025.
Copyright: © 2025 Shao, Xin, Chen, Liu, Gu, Li and Pan. 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: Yuning Pan, fyypanyuning@nbu.edu.cn
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