Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study — Pete H. Gueldner (2025) | RDL Network
Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study
Article 2025 en
Authors
PG
Pete H. Gueldner
KK
Katherine E. Kerr
NL
Nathan L. Liang
Abstract
1 min read
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.
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