Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm

Applications in Engineering Science

卷:10

David A. Vorp¹  Timothy K. Chung²  Nathan L. Liang³ 

[1] Division of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States;

[2] Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States;

[3] Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States;

关键词: Abdominal aortic aneurysm;    Artificial intelligence;    Machine learning;    Stress analysis;    Finite element analysis;    Automation;   

DOI  :  

来源: DOAJ

【 摘 要 】

Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their risk of rupture - which is the 13th 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).

【 授权许可】

Unknown