Aortic atherosclerosis evaluation using deep learning based on non-contrast CT: A retrospective multi-center study - Mingliang Yang; Jinhao Lyu; Yongqin Xiong; Aoxue Mei; Jianxing Hu; Yue Zhang; Xiaoyu Wang; Xiangbing Bian; Jiayu Huang; Runze Li; Xinbo Xing; Sulian Su; et al. (iScience)
Summary: 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.