A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study

作者：Zhongping Guo, Ying Liu, Jingxu Xu, Chencui Huang, Fandong Zhang, Chongchang Miao, Yonggang Zhang, Mengshuang Li, Hangsheng Shan, Yan Gu · 发表于：Frontiers in Neurology · 年份：2025 · DOI：10.3389/fneur.2024.1480792 · 被引用次数：7 · 研究领域：Cerebrovascular and Carotid Artery Diseases、Retinal Imaging and Analysis、Artificial Intelligence in Healthcare

Objective To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. Methods We retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model’s diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios. Results In total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 7...