Machine learning method for extracting elastic modulus of cells.
Cell
Elastic modulus
Finite element simulation
Machine learning
Neural networks
Journal
Biomechanics and modeling in mechanobiology
ISSN: 1617-7940
Titre abrégé: Biomech Model Mechanobiol
Pays: Germany
ID NLM: 101135325
Informations de publication
Date de publication:
Oct 2022
Oct 2022
Historique:
received:
03
01
2022
accepted:
01
07
2022
pubmed:
25
8
2022
medline:
4
11
2022
entrez:
24
8
2022
Statut:
ppublish
Résumé
The Hertz contact mechanics model is commonly used to extract the elastic modulus of the cell, but the basic assumptions of the model are often not met in cell indentation experiments, which can lead to errors in the obtained elastic modulus of cell. The establishment of theoretical formulas or modification of the Hertz formulas has been proposed to reduce the errors introduced by indentation depth and cell thickness, but errors from cell radius and probe radius are largely neglected. Herein, we build a neural network model in machine learning to extract the elastic modulus of cell, which takes into account of four variables: indentation depth, cell thickness, cell radius, and probe radius. The validity of the model is demonstrated by the indentation experiment. The introduction of machine learning methods provides an alternative solution for extracting the elastic modulus of the cell and has potential for application.
Identifiants
pubmed: 36001275
doi: 10.1007/s10237-022-01609-x
pii: 10.1007/s10237-022-01609-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1603-1612Subventions
Organisme : Key Technologies Research and Development Program
ID : 2018YFA0704103
Organisme : Key Technologies Research and Development Program
ID : 2018YFA0704104
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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