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Machine Learning Prediction of Tissue Strength and Local Rupture Risk in Ascending Thoracic Aortic Aneurysms
Journal article   Open access

Machine Learning Prediction of Tissue Strength and Local Rupture Risk in Ascending Thoracic Aortic Aneurysms

Xuehuan He, Stephane Avril and Jia Lu
Molecular & cellular biomechanics, Vol.16(S2), pp.50-52
2019
DOI: 10.32604/mcb.2019.07390
url
https://doi.org/10.32604/mcb.2019.07390View
Published (Version of record) Open Access

Abstract

A Multi-layer Perceptron (MLP) neural network model [1] is developed to predict the strength of ascending thoracic aortic aneurysm (ATAA) tissues using tension-strain data and assess local rupture risk. The data were collected through in vitro inflation tests on ATAA samples from 12 patients who underwent surgical intervention [2]. An inverse stress analysis was performed to compute the wall tension at Gauss points. Some of these Gauss points are at or near sites where the samples eventually ruptured, while others are at locations where the tissue remained intact. A total of 27,648 tension- strain curves, including 26,676 2223 nonrupture and 972 rupture were garnered and each fitted to a third order NURBS function with 3 knot intervals. A typical fitted curve is shown in Fig. 1, which has a J-shape with a compliant elastin-dominated region at the low strain range, a transition phase in the middle, and a stiff collagen-dominated region at the high strain end. Eight features associated with the low strain region response are extracted: the tension, strain, slope and curvature at the maximum curvature point and a transition point (Figure 1). These features are subsequently fed to the machine learning model.

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