Carotid artery plaques can cause arterial vascular diseases such as stroke and myocardial infarction, posing a severe threat to human life. However, the current clinical examination mainly relies on a direct assessment by physicians of patients' clinical indicators and medical images, lacking an integrated visualization tool for analyzing the influencing factors and composition of carotid artery plaques. We have designed an intelligent carotid artery plaque visual analysis system for vascular surgery experts to comprehensively analyze the clinical physiological and imaging indicators of carotid artery diseases. The system mainly includes two functions: First, it displays the correlation between carotid artery plaque and various factors through a series of information visualization methods and integrates the analysis of patient physiological indicator data. Second, it enhances the interface guidance analysis of the inherent correlation between the components of carotid artery plaque through machine learning and displays the spatial distribution of the plaque on medical images. Additionally, we conducted two case studies on carotid artery plaques using real data obtained from a hospital, and the results indicate that our designed carotid analysis system can effectively provide clinical diagnosis and treatment guidance for vascular surgeons.

翻译：颈动脉斑块可引发卒中和心肌梗死等动脉血管疾病，严重威胁人类生命。然而，当前临床检查主要依赖医生对患者临床指标和医学影像的直接评估，缺乏用于分析颈动脉斑块影响因素及组成的集成化可视化工具。我们为血管外科专家设计了一套智能颈动脉斑块可视化分析系统，用于综合分析颈动脉疾病的临床生理与影像指标。该系统主要包括两项功能：其一，通过系列信息可视化方法展示颈动脉斑块与多种因素的关联性，并集成分析患者生理指标数据；其二，通过机器学习增强对颈动脉斑块各成分内在关联性的界面引导分析，并在医学影像上呈现斑块的空间分布。此外，我们使用医院获取的真实数据开展了两个颈动脉斑块案例研究，结果表明所设计的颈动脉分析系统可有效为血管外科医生提供临床诊疗指导。