Visualization analysis of research hotspots and trends in Type B Aortic Dissection based on bibliometrics.

PURPOSE: This study aims to systematically map and analyze the knowledge structure, research hotspots, and evolutionary trends in the field of Type B Aortic Dissection (TBAD) using bibliometric methods. PATIENTS AND METHODS: CiteSpace and VOSviewer were employed to conduct quantitative and visual analyses across multiple dimensions, including publication trends, collaboration networks among countries/institutions/authors, keyword co-occurrence and evolution, as well as co-citation analysis and reference burst detection. RESULTS: The number of publications in this field has shown an overall upward trend, with a significant increase since 2013, reflecting the growing clinical and scientific attention to this life-threatening vascular emergency. The United States (USA) and China form a dual-core global collaboration structure, with the USA leading in international cooperation depth and citation impact, and China emerging as a major contributor with rapid growth in research output - an evolution that mirrors the global redistribution of cardiovascular research capacity. Keyword analysis reveals a paradigm shift from technical application to precision intervention and individualized management, while current research focusing on Thoracic Endovascular Aortic Repair (TEVAR), complication prediction, computational fluid dynamics (CFD), and artificial intelligence (AI)-assisted diagnosis and treatment. Co-citation analysis confirms TEVAR as the gold-standard minimally invasive treatment for TBAD, with its widespread acceptance driving the standardization of clinical practice. Burst analysis of keywords indicates that "prediction model" and "deep learning" have become emerging research hotspots, marking the entry of TBAD research into an intelligent, data-driven era. CONCLUSION: Research on TBAD has developed a sophisticated knowledge system over the past two decades, shifting from traditional surgical exploration to an intelligent, data-driven research paradigm. This bibliometric analysis identifies a USA-China dual-core global collaboration pattern in the field and a three-stage evolution of research focus from pathophysiological exploration to evidence-based TEVAR standardization, and further to the integration of CFD and AI. Critical research gaps are also highlighted, including under-investigation of high-risk populations, insufficient long-term evidence for TEVAR, and inadequate cross-disciplinary integration of CFD, AI and genomics. Future TBAD research should prioritize multicenter prospective trials to upgrade clinical evidence, advance interdisciplinary precision medicine models, and build globally standardized big data platforms for the development and validation of AI-based diagnostic and therapeutic tools, thereby achieving more scientific and personalized management of TBAD.