Robbi Erich, Ravanelli Daniele, Allievi Sara, Raunig Igor, Bonvini Stefano, Passerini Andrea, Trianni Annalisa
Department of Information Engineering and Computer Sciences, DISI of University of Trento, Via Sommarive, Trento, 38123, Italy.
Medical Physics Department of Provincial Agency for Health Services of the Autonomous Province of Trento, APSS, S. Chiara Hospital, Trento, 38121, Italy.
Sci Rep. 2025 May 12;15(1):16431. doi: 10.1038/s41598-025-00484-7.
Endovascular Aneurysm Repair (EVAR) is a minimally invasive procedure crucial for treating abdominal aortic aneurysms (AAA), where precise pre-operative planning is essential. Current clinical methods rely on manual measurements, which are time-consuming and prone to errors. Although AI solutions are increasingly being developed to automate aspects of these processes, most existing approaches primarily focus on computing volumes and diameters, falling short of delivering a fully automated pre-operative analysis. This work presents BRAVE (Blood Vessels Recognition and Aneurysms Visualization Enhancement), the first comprehensive AI-driven solution for vascular segmentation and AAA analysis using pre-operative CTA scans. BRAVE offers exhaustive segmentation, identifying both the primary abdominal aorta and secondary vessels, often overlooked by existing methods, providing a complete view of the vascular structure. The pipeline performs advanced volumetric analysis of the aneurysm sac, quantifying thrombotic tissue and calcifications, and automatically identifies the proximal and distal sealing zones, critical for successful EVAR procedures. BRAVE enables fully automated processing, reducing manual intervention and improving clinical workflow efficiency. Trained on a multi-center open-access dataset, it demonstrates generalizability across different CTA protocols and patient populations, ensuring robustness in diverse clinical settings. This solution saves time, ensures precision, and standardizes the process, enhancing vascular surgeons' decision-making.
血管内动脉瘤修复术(EVAR)是一种微创手术,对治疗腹主动脉瘤(AAA)至关重要,术前精确规划必不可少。目前的临床方法依赖于手动测量,既耗时又容易出错。尽管越来越多地开发人工智能解决方案来使这些过程的某些方面自动化,但大多数现有方法主要侧重于计算体积和直径,无法提供完全自动化的术前分析。这项工作提出了BRAVE(血管识别与动脉瘤可视化增强),这是第一个使用术前CTA扫描进行血管分割和AAA分析的全面的人工智能驱动解决方案。BRAVE提供详尽的分割,识别主要腹主动脉和二级血管,而这些通常被现有方法忽视,从而提供血管结构的完整视图。该流程对动脉瘤囊进行高级体积分析,量化血栓组织和钙化,并自动识别近端和远端密封区,这对成功的EVAR手术至关重要。BRAVE实现了完全自动化处理,减少了人工干预,提高了临床工作流程效率。在多中心开放获取数据集上进行训练,它证明了在不同CTA协议和患者群体中的通用性,确保了在各种临床环境中的稳健性。该解决方案节省时间,确保精度,并使过程标准化,增强了血管外科医生的决策能力。