Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study - Pete H. Gueldner, BS; Katherine E. Kerr, BSBME; Nathan Liang, MD; Timothy K. Chung, PhD; Tiziano Tallarita, MD; Joe Wildenberg, MD; Jason Beckermann, MD; David A. Vorp, PhD; Indrani Sen, MD (Journal of Vascular Surgery Cases and Innovative Techniques)
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.