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Using Deep Learning and Macroscopic Imaging of Porcine Heart Valve Leaflets to Predict Uniaxial Stress-Strain Responses
Luis Victor · CJ Barberan · Richard Baraniuk · Jane Grande-Allen

Heart valves consist of leaflets that can degrade due to a range of disease processes. To better design prostheses, it is critical to study leaflet mechanics. Although mechanical testing of heart valve leaflets (HVLs) is the standard for evaluating mechanical behavior, imaging and deep learning (DL) networks, such as convolutional neural networks (CNNs), are more readily available and cost-effective. In this work, we determined the influence that a dataset that we curated had on the ability of a CNN to predict the stress-strain response of the leaflets. Our findings indicate that CNNs can be used to predict the polynomial coefficients needed for reconstructing the toe and linear regions of typically observed mechanical behavior, which lie near the physiological strain, 10\% strain.

Author Information

Luis Victor (Rice University)
CJ Barberan (Rice University)
Richard Baraniuk (Rice University)
Jane Grande-Allen (Rice University)

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