Aortic atherosclerosis evaluation using deep learning based on non-contrast CT: A retrospective multi-center study.
Authors
Affiliations (4)
Affiliations (4)
- School of Medical Technology, Beijing Institute of Technology, No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China.
- Department of Radiology, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, Beijing 100853, China.
- Department of Radiology, Xiangyang NO.1 People's Hospital, Hubei University of Medicine, No. 15 Jiefang Road, Fancheng District, Xiangyang 441000, China.
- Department of Radiology, Xiamen Humanity Hospital Fujian Medical University, No. 3777 Xianyue Road, Huli District, Xiamen City, Fujian Province 361000, China.
Abstract
Non-contrast CT (NCCT) is widely used in clinical practice and holds potential for large-scale atherosclerosis screening, yet its application in detecting and grading aortic atherosclerosis remains limited. To address this, we propose Aortic-AAE, an automated segmentation system based on a cascaded attention mechanism within the nnU-Net framework. The cascaded attention module enhances feature learning across complex anatomical structures, outperforming existing attention modules. Integrated preprocessing and post-processing ensure anatomical consistency and robustness across multi-center data. Trained on 435 labeled NCCT scans from three centers and validated on 388 independent cases, Aortic-AAE achieved 81.12% accuracy in aortic stenosis classification and 92.37% in Agatston scoring of calcified plaques, surpassing five state-of-the-art models. This study demonstrates the feasibility of using deep learning for accurate detection and grading of aortic atherosclerosis from NCCT, supporting improved diagnostic decisions and enhanced clinical workflows.