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Performance of personalised prosthesis under static pressure: Numerical analysis and experimental validation
Masoud Mohseni-Dargah1, Christopher Pastras2, Payal Mukherjee3
1School of Engineering, Macquarie University, Sydney, NSW, 2109, Australia; Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
Abstract:
This study investigates the performance of personalised middle ear prostheses under static pressure through a combined approach of numerical analysis and experimental validation. The sound transmission performances of both normal and reconstructed middle ears undergo changes under high positive or negative pressure within the middle ear cavity. This pressure fluctuation has the potential to result in prosthesis displacement/extrusion in patients. To optimise the design of middle ear prostheses, it is crucial to consider various factors, including the condition of the middle ear cavity in which the prosthesis is placed. The integration of computational modelling techniques with non-invasive imaging modalities has demonstrated significant promise and distinct prospects in middle ear surgery. In this study, we assessed the efficacy of Finite Element (FE) analysis in modelling the responses of both normal and reconstructed middle ears to elevated static pressure within the ear canal. The FE model underwent validation using experimental data derived from human cadaveric temporal bones before progressing to subsequent investigations. Afterwards, we assessed stapes and umbo displacements in the reconstructed middle ear under static pressure, with either a columella-type prosthesis or a prosthetic incus, closely resembling a healthy incus. Results indicated the superior performance of the prosthetic incus in terms of both sound transmission to the inner ear and stress distribution patterns on the TM, potentially lowering the risk of prosthesis displacement/extrusion. This study underscores the potential of computational analysis in middle ear surgery, encompassing aspects such as prosthesis design, predicting outcomes in ossicular chain reconstruction (OCR), and mitigating experimental costs.
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