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A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves
Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations.
Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
When using all the six measurements, it is found that maximum F1 scores larger than 0. 9 are achieved for CAS and PAD, larger than 0. 85 for SAS, and larger than 0. 98 for both low- and high-severity AAAs.
A proof of concept study for machine learning application to stenosis detection
This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs.
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