Publisher DOI: 10.1016/j.actbio.2022.05.039
Title: Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning
Language: English
Authors: Linka, Kevin 
Cavinato, Cristina 
Humphrey, Jay Dowell 
Cyron, Christian J. 
Keywords: arterial tissues; explainable AI; hybrid modeling; tissue maturation
Issue Date: 2022
Source: Acta Biomaterialia (in Press) : (2022)
Abstract (english): 
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:
URI: http://hdl.handle.net/11420/12920
ISSN: 1742-7061
Journal: Acta biomaterialia 
Institute: Kontinuums- und Werkstoffmechanik M-15 
Document Type: Article
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