Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning

Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy (R2=0.92) the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein. Statement of significance:

Subjects

arterial tissues

explainable AI

hybrid modeling

tissue maturation

MLE@TUHH

DDC Class

620: Ingenieurwissenschaften

More Funding Information

This work was supported, in part, by grants from the US NIH (R01 HL105297, U01 HL142518).

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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1-s2.0-S1742706122003154-main.pdf

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2.45 MB

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Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning