{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/aneupy-an-open-source-python-tool-for","title":"AneuPy: An open source Python tool for creating simulation-ready geometries of abdominal aortic aneurysms","arxiv_id":"2504.15285","date":"2025-03-13","proceeding":null,"authors":["Mario de Lucio","Jacobo Diaz","Alberto de Castro","Luis E. Romera"],"abstract":"Abdominal aortic aneurysms (AAAs) are localized dilatations of the abdominal aorta that can lead to life-threatening rupture if left untreated. AAAs primarily affect older individuals, with high mortality rates following rupture, so early diagnosis and risk assessment are critical. The geometrical characteristics of an AAA, such as its maximum diameter, asymmetry, and wall thickness, are extremely significant in biomechanical models for the assessment of rupture risk. Despite the growing use of computational modeling for AAA investigation, there is a notable gap in accessible, open-source software capable of generating simulation-ready geometries for biomechanical and hemodynamic simulations. To address this gap, we introduce \\textbf{AneuPy}, an open-source Python-based tool designed to create both idealized and patient-specific AAA geometric models. \\textbf{AneuPy} is a fast and automated approach for generating aneurysm geometries from minimal input data, allowing for extensive parameter customization. By automating the creation of simulation-ready geometries for finite element analysis (FEA), computational fluid dynamics (CFD), or fluid-structure interaction (FSI) models, \\textbf{AneuPy} can facilitate research in AAA and improve patient-specific risk prediction.","url_abs":"https://arxiv.org/abs/2504.15285v2","url_pdf":"https://arxiv.org/pdf/2504.15285v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"aneupy-an-open-source-python-tool-for","repo_url":"https://github.com/mdeluci/AneuPy","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}