ArticleScientific reports2025
Automatic CTA analysis for blood vessels and aneurysm features extraction in EVAR planning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Multi-centre Validation of Automated Outer-to-Outer Wall Segmentation of Infrarenal Abdominal Aortic Aneurysms Using a nnU-Net Based Model.Journal of imaging informatics in medicine · 2026Article
- Mitochondrial cardiovascular diseases: molecular mechanisms, multi-omics exploration and therapeutic strategies.Journal of advanced research · 2026Review
- Validation of a Deep Learning U-Net Algorithm for Multistructure Segmentation of Infrarenal Abdominal Aortic Aneurysms including Lumen, Thrombus, and Calcifications.EJVES vascular forum · 2026Article
- A multimodal deep learning model for predicting impending rupture in symptomatic abdominal aortic aneurysms using CTA and clinical data.Frontiers in cardiovascular medicine · 2026Article
- Automatic Segmentation of Intraluminal Thrombus in Abdominal Aortic Aneurysms Based on CT Images: A Comprehensive Review of Deep Learning-Based Methods.Journal of clinical medicine · 2025Review
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.