Zhongping Guo ¹ Ying Liu ¹ Jingxu Xu ² Chencui Huang ² Zhang Fandong ^3* Chongchang Miao ^1* Yonggang Zhang ^1* Mengshuang Li ^1* Hangsheng Shan ^1* Gu Yan ^1*

• ¹ Department of Radiology, Lianyungang Clinical College of Nanjing Medical University/ The First People’s Hospital of Lianyungang, Lianyungang, China
• ² Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
• ³ Deepwise Artificial Intelligence (AI) Lab, Deepwise, Beijing, China

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%, 79.17%). This model also analyzed the results of different types of plaques and shows good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00%, 86.87%, 84.29%). Especially, in the clinical application scenario analysis, the model's diagnostic results for plaques were found to be higher than those of four out of six radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists' sensitivity in diagnosing plaques. Additionally, the model's diagnostic time for plaques (6s) was found to be significantly shorter than that all of radiologists (p < 0.001).This AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.