Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Vascular Surgery Cases and Innovative Techniques |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468428725000887 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Pete H. Gueldner, BS |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a64a9ac40ae45509fce8a070ea1a4f1 |
| institution | Kabale University |
| issn | 2468-4287 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Vascular Surgery Cases and Innovative Techniques |
| spelling | doaj-art-3a64a9ac40ae45509fce8a070ea1a4f12025-08-20T03:56:45ZengElsevierJournal of Vascular Surgery Cases and Innovative Techniques2468-42872025-08-0111410180610.1016/j.jvscit.2025.101806Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case studyPete H. Gueldner, BS0Katherine E. Kerr, BSBME1Nathan Liang, MD2Timothy K. Chung, PhD3Tiziano Tallarita, MD4Joe Wildenberg, MD5Jason Beckermann, MD6David A. Vorp, PhD7Indrani Sen, MD8Department of Bioengineering, University of Pittsburgh, Pittsburgh, PADepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PADepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Division of Vascular Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA; Department of Surgery, University of Pittsburgh, Pittsburgh, PADepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Clinical & Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PADepartment of Vascular Surgery, Mayo Clinic Health Systems, Eau Claire, WIDepartment of Radiology, Mayo Clinic Health Systems, Eau Claire, WIDepartment of Vascular Surgery, Mayo Clinic Health Systems, Eau Claire, WIDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Department of Surgery, University of Pittsburgh, Pittsburgh, PA; Clinical & Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA; Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA; Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA; Magee Women's Research Institute, University of Pittsburgh, Pittsburgh, PA; Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA; Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PADepartment of Vascular Surgery, Mayo Clinic Health Systems, Eau Claire, WI; Correspondence: Indrani Sen, MD, Division of Vascular Surgery, Mayo Clinic Health System, 1221 Whipple St, Eau Claire, WI 54701Analyzing 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.http://www.sciencedirect.com/science/article/pii/S2468428725000887Juxtarenal aneurysmBiomechanicsWall stressFenestrated repair |
| spellingShingle | 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 Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study Journal of Vascular Surgery Cases and Innovative Techniques Juxtarenal aneurysm Biomechanics Wall stress Fenestrated repair |
| title | Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study |
| title_full | Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study |
| title_fullStr | Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study |
| title_full_unstemmed | Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study |
| title_short | Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study |
| title_sort | artificial intelligence based machine learning protocols enable quicker assessment of aortic biomechanics a case study |
| topic | Juxtarenal aneurysm Biomechanics Wall stress Fenestrated repair |
| url | http://www.sciencedirect.com/science/article/pii/S2468428725000887 |
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