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Get Free AccessAnalyzing aortic biomechanical wall stresses for abdominal aortic aneurysms remains challenging. Clinical applications of biomechanical and morphological image-based analysis protocols have limited adoption owing to the time and expertise required. Our multidisciplinary and multi-institute team has demonstrated the feasibility of expediting advanced aortic image analysis on a single patient tracked longitudinally. We also demonstrate the utility of a previously trained artificial intelligence-based classifier that accurately predicts patient outcomes, a potential alternative to serial surveillance. This paper describes the overall workflow and processes performed in a 70-year-old man who was incidentally diagnosed to have a 5.4-cm juxtarenal aortic aneurysm in 2016 with successful fenestrated endovascular repair in 2023.
Pete H. Gueldner, Katherine E. Kerr, Nathan L. Liang, Timothy K. Chung, Tiziano Tallarita, Joseph C. Wildenberg, Jason Beckermann, David Vorp, Indrani Sen (2025). Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study. , 11(4), DOI: https://doi.org/10.1016/j.jvscit.2025.101806.
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Type
Article
Year
2025
Authors
9
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.jvscit.2025.101806
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