loading page

Addressing Model Uncertainties in Finite Element Simulation of Electrically Stimulated Implants for Critical-Size Mandibular Defects
  • Hendrikje Raben,
  • Peer Wolfgang Kämmerer,
  • Ursula van Rienen
Hendrikje Raben

Corresponding Author:[email protected]

Author Profile
Peer Wolfgang Kämmerer
Ursula van Rienen


Objective: Electrical stimulation is known to enhance bone healing. Novel electrostimulating devices are currently being developed for the treatment of critical-size bone defects in the mandible. Previous numerical models of these devices did not account for possible uncertainties in the input data. We present the numerical model of an electrically stimulated minipig mandible, including optimization and uncertainty quantification (UQ) methods that allow us to determine the most influential parameters. Methods: Uncertainties in the optimized finite element model are quantified using the polynomial chaos method that is implemented in the open-source Python toolbox Uncertainpy. The volumes of understimulated, beneficially stimulated, and overstimulated tissue are considered quantities of interest because they may significantly impact the expected healing success. Further, the current is a substantial quantity, limiting the lifetime of a battery-driven stimulation unit. With sensitivity analyses, the most critical parameters in the numerical model can be identified. Thus, we can learn which parameters are particularly relevant, for example, when conceptualizing the stimulation unit or planning the manufacturing process. Results: The results of this study show that the parameters of the electrodetissue interface (ETI), as well as the conductivity within the defect volume, have the most significant impact on the model results. Conclusions: The UQ results suggest that careful characterization of the ETI and the dielectric tissue properties is crucial to reduce these uncertainties. Significance: The numerical model regarding uncertainties yields important implications for reliable implant design and clinical translation.
25 Mar 2024Submitted to TechRxiv
30 Mar 2024Published in TechRxiv