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Updated: Feb 14, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Simulating localised cellular inflammation and substrate properties in a strain energy density based bone remodelling
Naomi Rosenberg1, Anthony M J Bull1
1a Department of Bioengineering , Imperial College London , London , UK.
Abstract:
Bone responds to mechanical stimulus and a range of pre-existing finite element models have been suggested to reproduce the internal physiological structure of bone. Inflammation effects are not included in these models, yet inflammation is a key component of bone repair in trauma. Therefore, a model is proposed and tested here that extends these methods to include parameters that could be considered to represent the behaviour of bone remodelling when influenced by inflammation. The proposed model regulates remodelling based on findings from recent studies into the nature of heterotopic ossification, the formation of heterotopic bone, which have revealed information about the nature of bone after high levels of trauma. These parameters include consideration of the distance from the zone of trauma, the density of mesenchymal stem cells, and substrate stiffness as a trigger for cells becoming osteogenic. The method is tested on a two-dimensional plate model and shows that the new extended algorithm can produce a range of structures depending on inputs that could be used in the future to replicate physiological scenarios.
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