Liu Kun, Zhao Deyin, Feng Lvfan, Zhang Zhaoxuan, Qiu Peng, Wu Xiaoyu, Wang Ruihua, Hussain Azad, Uzokov Jamol, Han Yanshuo
Department of Cardiac Surgery, Affiliated Hospital, Guizhou Medical University, Guiyang, China.
Second Ward of General Surgery, Suzhou Municipal Hospital of Anhui Province, Suzhou, China.
Hellenic J Cardiol. 2025 Jan-Feb;81:49-64. doi: 10.1016/j.hjc.2024.08.006. Epub 2024 Aug 10.
Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through machine learning clustering analysis of cardiovascular computed tomography (CT) imaging.
Electronic medical records were collected to extract demographic and clinical features of patients with TBAD. Exclusion criteria ensured homogeneity and clinical relevance of the TBAD cohort. Controls were selected on the basis of age, comorbidity status, and imaging availability. Aortic morphological parameters were extracted from CT angiography and subjected to K-means clustering analysis to identify distinct phenotypes.
Clustering analysis revealed three phenotypes of patients with TBAD with significant correlations with population characteristics and dissection rates. This pioneering study used CT-based three-dimensional reconstruction to classify high-risk individuals, demonstrating the potential of machine learning in enhancing diagnostic accuracy and personalized treatment strategies. Recent advancements in machine learning have garnered attention in cardiovascular imaging, particularly in aortic dissection research. These studies leverage various imaging modalities to extract valuable features and information from cardiovascular images, paving the way for more personalized interventions.
This study provides insights into the phenotypic heterogeneity of patients with TBAD using machine learning clustering analysis of cardiovascular CT imaging. The identified phenotypes exhibit correlations with population characteristics and dissection rates, highlighting the potential of machine learning in risk stratification and personalized management of aortic dissection. Further research in this field holds promise for improving diagnostic accuracy and treatment outcomes in patients with aortic dissection.
主动脉夹层仍然是一种危及生命的疾病，需要准确诊断和及时干预。本研究旨在通过对心血管计算机断层扫描（CT）成像进行机器学习聚类分析，探讨B型主动脉夹层（TBAD）患者的表型异质性。
收集电子病历以提取TBAD患者的人口统计学和临床特征。排除标准确保了TBAD队列的同质性和临床相关性。根据年龄、合并症状态和成像可用性选择对照。从CT血管造影中提取主动脉形态学参数，并进行K均值聚类分析以识别不同的表型。
聚类分析揭示了TBAD患者的三种表型，与人群特征和夹层发生率显著相关。这项开创性研究使用基于CT的三维重建对高危个体进行分类，证明了机器学习在提高诊断准确性和个性化治疗策略方面的潜力。机器学习的最新进展在心血管成像领域引起了关注，尤其是在主动脉夹层研究中。这些研究利用各种成像方式从心血管图像中提取有价值的特征和信息，为更个性化的干预铺平了道路。
本研究通过对心血管CT成像进行机器学习聚类分析，深入了解了TBAD患者的表型异质性。所识别的表型与人群特征和夹层发生率相关，突出了机器学习在主动脉夹层风险分层和个性化管理中的潜力。该领域的进一步研究有望提高主动脉夹层患者的诊断准确性和治疗效果。
