RESUMEN
BACKGROUND: Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to unravel phenotypic heterogeneity in Stanford type B aortic dissection (TBAD) patients through machine learning clustering analysis of cardiovascular CT imaging. METHODS: Electronic medical records were collected to extract demographic and clinical features of TBAD patients. Exclusion criteria ensured homogeneity and clinical relevance of the TBAD cohort. Controls were selected based on age, comorbidity status, and imaging availability. Aortic morphological parameters were extracted from CT angiography (CTA) and subjected to k-means clustering analysis to identify distinct phenotypes. RESULTS: Clustering analysis revealed three phenotypes of TBAD patients with significant correlations to population characteristics and dissection rates. This pioneering study utilized 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. CONCLUSIONS: This study provides insights into the phenotypic heterogeneity of TBAD patients 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 aortic dissection patients.