Suppr超能文献

基于人工智能的机器学习协议可实现对主动脉生物力学的更快评估:一项案例研究。

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

作者信息

Gueldner Pete H, Kerr Katherine E, Liang Nathan, Chung Timothy K, Tallarita Tiziano, Wildenberg Joe, Beckermann Jason, Vorp David A, Sen Indrani

机构信息

Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA.

Division of Vascular Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA.

出版信息

J Vasc Surg Cases Innov Tech. 2025 Apr 14;11(4):101806. doi: 10.1016/j.jvscit.2025.101806. eCollection 2025 Aug.

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.

摘要

分析腹主动脉瘤的主动脉生物力学壁应力仍然具有挑战性。基于生物力学和形态学图像的分析方案在临床应用中的采用率有限,因为这需要时间和专业知识。我们的多学科、多机构团队已经证明了对一名纵向跟踪的患者快速进行高级主动脉图像分析的可行性。我们还展示了一个先前训练的基于人工智能的分类器的效用,该分类器能够准确预测患者的预后,这是一种替代连续监测的潜在方法。本文描述了一名70岁男性的总体工作流程和过程,该男性于2016年偶然被诊断出患有5.4厘米的肾旁主动脉瘤,并于2023年成功进行了开窗血管内修复。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f498/12141894/fb2438abf69f/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验