Chung Timothy K, Liang Nathan L, Vorp David A
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.
Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States.
Appl Eng Sci. 2022 Jun;10. doi: 10.1016/j.apples.2022.100104. Epub 2022 May 2.
Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their risk of rupture - which is the 13 leading cause of death in the US - exceeds the risks associated with repair. Clinical intervention occurs when an aneurysm diameter exceeds 5.5 cm, but this "one-size fits all" criterion is insufficient, as it has been reported thatup to a quarter of AAA smaller than 5.5 cm do rupture. Therefore, there is a need for a more reliable, patient-specific, clinical tool to aide in the management of AAA. Biomechanical assessment of AAA is thought to provide critical physical insights to rupture risk, but clinical translataion of biomechanics-based tools has been limited due to the expertise, time, and computational requirements. It was estimated that through 2015, only 348 individual AAA cases have had biomechanical stress analysis performed, suggesting a deficient sample size to make such analysis relevant in the clinic. Artificial intelligence (AI) algorithms offer the potential to increase the throughput of AAA biomechanical analyses by reducing the overall time required to assess the wall stresses in these complex structures using traditional methods. This can be achieved by automatically segmenting regions of interest from medical images and using machine learning models to predict wall stresses of AAA. In this study, we present an automated AI-based methodology to predict the biomechanical wall stresses for individual AAA. The predictions using this approach were completed in a significantly less amount of time compared to a more traditional approach (~4 hours vs 20 seconds).
腹主动脉瘤（AAA）已被深入研究，以了解其破裂风险（在美国，破裂是主要死因之一）何时超过修复相关风险。当动脉瘤直径超过5.5厘米时进行临床干预，但这种“一刀切”的标准并不充分，因为据报道，直径小于5.5厘米的腹主动脉瘤中，多达四分之一会破裂。因此，需要一种更可靠、针对患者的临床工具来辅助腹主动脉瘤的管理。腹主动脉瘤的生物力学评估被认为能为破裂风险提供关键的物理见解，但基于生物力学的工具在临床转化方面受到专业知识、时间和计算要求的限制。据估计，到2015年，仅有348例个体腹主动脉瘤病例进行了生物力学应力分析，这表明样本量不足，无法使此类分析在临床上具有相关性。人工智能（AI）算法有可能通过减少使用传统方法评估这些复杂结构壁应力所需的总时间，提高腹主动脉瘤生物力学分析的通量。这可以通过从医学图像中自动分割感兴趣区域，并使用机器学习模型预测腹主动脉瘤的壁应力来实现。在本研究中，我们提出了一种基于人工智能的自动化方法，来预测个体腹主动脉瘤的生物力学壁应力。与更传统的方法相比，使用这种方法进行预测所需的时间显著减少（约4小时对20秒）。
