Evidence map›Paper›PMID 35364365›Full record

ArticleJournal of the mechanical behavior of biomedical materials2022

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Yiliang Wang, Jamshid Ghaboussi, Cameron Hoerig, Michael F Insana

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the mechanical behavior of biomedical materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yiliang WangDepartment of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States of America; Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States of America. Electronic address: wang513@illinois.edu.
Jamshid GhaboussiDepartment of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States of America.
Cameron HoerigF. L. Lizzi Center for Biomedical Engineering, Riverside Research Institute, New York, NY 10038, United States of America.
Michael F InsanaBeckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States of America; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States of America.

Funding

New Approach to US Elasticity ImagingR21EB023402 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI INSANA, MICHAEL F · 2017 to 2018
$400k
NIBIB NIH HHS R21 EB023402
6 · The paper itself

Abstract

The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications of AutoP in tissue-like media have focused on linear elastic mechanical behavior as the target object is infinitesimally compressed. In this study, we extended the application of AutoP in characterizing nonlinear elastic mechanical behavior as the target object undergoes finite compressive deformation. Guided by the prior of nonlinear media, we modified the training data generated by AutoP to speed its ability to learn to model deformations. AutoP training was validated using both synthetic and experimental data recorded from 3D objects. Force-displacement measurements were obtained using ultrasonic imaging from heterogeneous agar-gelatin phantoms. Measurement on samples of phantom components were analyzed to obtain independent measurements of material properties. Comparisons validated the material properties found from neural network constitutive models (NNCMs) trained using AutoP. Results were found to be robust to measurement errors and spatial variations in material properties.

Indexed as

Neural Networks, ComputerNonlinear DynamicsAgarElasticityFinite Element AnalysisModels, BiologicalPhantoms, ImagingStress, MechanicalAgar3-D modelsInverse methodsMachine learningProperty estimation

Identifiers

PMID35364365
PMCPMC9035135

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.