Prediction of local strength of ascending thoracic aortic aneurysms. (March 2021)

• Record Type: Journal Article
• Authors: He, Xuehuan; Avril, Stephane; Lu, Jia
• Is Part Of: Journal of the mechanical behavior of biomedical materials. Volume 115(2021)
• Publication Date: 2021-03
• Subjects: ATAA -- Strength -- Rupture risk -- Machine learning; Biomedical materials -- Periodicals; Biomedical materials -- Mechanical properties -- Periodicals; Biomedical materials; Biomedical materials -- Mechanical properties; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.jmbbm.2020.104284
• Languages: English
• ISSNs: 1751-6161

Abstract

Abstract: Knowledges of both local stress and strength are needed for a reliable evaluation of the rupture risk for ascending thoracic aortic aneurysm (ATAA). In this study, machine learning is applied to predict the local strength of ATAA tissues based on tension-strain data collected through in vitro inflation tests on tissue samples. Inputs to machine learning models are tension, strain, slope, and curvature values at two points on the low strain region of the tension-strain curve. The models are trained using data from locations where the tissue ruptured, and subsequently applied to data from intact sites to predict the local rupture strength. The predicted strengths are compared with the known strength at rupture sites as well as the highest tension the tissues experienced at the intact sites. A local rupture index, which is the ratio of the end tension to the predicted rupture strength, is computed. The 'hot spots' of the rupture index are found to match the rupture sites better than those of the peak tension. The study suggests that the strength of ATAA tissue could be reliably predicted from early phase response features defined in this work.