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  5. Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study

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Article
en
2025

Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study

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en
2025
Vol 11 (4)
Vol. 11
DOI: 10.1016/j.jvscit.2025.101806

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David Vorp
David Vorp

University of Pittsburgh

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Pete H. Gueldner
Katherine E. Kerr
Nathan L. Liang
+6 more

Abstract

Analyzing 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.

How to cite this publication

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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Publication Details

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Article

Year

2025

Authors

9

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0

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Language

en

DOI

https://doi.org/10.1016/j.jvscit.2025.101806

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