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

    Xuehuan He1, Stephane Avril2, Jia Lu1,*

    Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 50-52, 2019, DOI:10.32604/mcb.2019.07390

    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… More >

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