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
{"title":"基于人工智能的机器学习协议能够更快地评估主动脉生物力学:一个案例研究","authors":"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","doi":"10.1016/j.jvscit.2025.101806","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":45071,"journal":{"name":"Journal of Vascular Surgery Cases Innovations and Techniques","volume":"11 4","pages":"Article 101806"},"PeriodicalIF":0.7000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study\",\"authors\":\"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\",\"doi\":\"10.1016/j.jvscit.2025.101806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":45071,\"journal\":{\"name\":\"Journal of Vascular Surgery Cases Innovations and Techniques\",\"volume\":\"11 4\",\"pages\":\"Article 101806\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2025-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Vascular Surgery Cases Innovations and Techniques\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468428725000887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Vascular Surgery Cases Innovations and Techniques","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468428725000887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"SURGERY","Score":null,"Total":0}
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 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.
期刊介绍:
Journal of Vascular Surgery Cases and Innovative Techniques is a surgical journal dedicated to publishing peer review high quality case reports, vascular images and innovative techniques related to all aspects of arterial, venous, and lymphatic diseases and disorders, including vascular trauma, malformations, wound care and the placement and maintenance of arterio-venous dialysis accesses with an emphasis on the practicing clinician. The Journal seeks to provide novel and timely information to vascular surgeons, interventionalists, phlebologists, wound care specialists, and allied health professionals involved with the management of patients with the entire spectrum of vascular disorders.