Mechanical Effects of Medical Device Attachment to Human Tympanic Membrane.

PURPOSE: Several treatment methods for hearing disorders rely on attaching medical devices to the tympanic membrane. This study aims to systematically analyze the effects of the material and geometrical properties and location of the medical devices attached to the tympanic membrane on middle-ear vibrations. METHODS: A finite-element model of the human middle ear was employed to simulate the effects of attachment of medical devices. Various types of material and geometrical properties, locations, and modeling scenarios were investigated for the medical device. RESULTS: The attachment of the device magnifies the effects of anti-resonances of the middle ear. Additionally, the variations of the material properties of the device significantly alter the middle-ear resonance frequency while changes in the umbo and stapes footplate motions are negligible at frequencies above 5 kHz. Furthermore, modeling the device as a point mass cannot accurately represent the implanted middle-ear behavior. The variations of the diameter and height of the medical device have negligible effects on the middle-ear vibrations at frequencies below 200 Hz but can have considerable impacts at higher frequencies. The effects of changing the device height were negligible at frequencies above 2 kHz. We also discuss the effects of medical device attachment on the vibration patterns of the tympanic membrane as well as the impacts of the variations of the location of the device on the stapes footplate responses. CONCLUSION: The findings of our study aid the development and optimization of new therapeutic devices, attached to the tympanic membrane, to have the least adverse effects on middle-ear vibrations.

Material characterization of human middle ear using machine-learning-based surrogate models.

This study aims to introduce a novel non-invasive method for rapid material characterization of middle-ear structures, taking into consideration the invaluable insights provided by the mechanical properties of ear tissues. Valuable insights into various ear pathologies can be gleaned from the mechanical properties of ear tissues, yet conventional techniques for assessing these properties often entail invasive procedures that preclude their use on living patients. In this study, in the first step, we developed machine-learning models of the middle ear to predict its responses with a significantly lower computational cost in comparison to finite-element models. Leveraging findings from prior research, we focused on the most influential model parameters: the Young's modulus and thickness of the tympanic membrane and the Young's modulus of the stapedial annular ligament. The eXtreme Gradient Boosting (XGBoost) method was implemented for creating the machine-learning models. Subsequently, we combined the created machine-learning models with Bayesian optimization (BoTorch) for fast and efficient estimation of the Young's moduli of the tympanic membrane and the stapedial annular ligament. We demonstrate that the resultant surrogate models can fairly represent the vibrational responses of the umbo, stapes footplate, and vibration patterns of the tympanic membrane at most frequencies. Also, our proposed material characterization approach successfully estimated the Young's moduli of the tympanic membrane and stapedial annular ligament (separately and simultaneously) with values of mean absolute percentage error of less than 7%. The remarkable accuracy achieved through the proposed material characterization method underscores its potential for eventual clinical applications of estimating mechanical properties of the middle-ear structures for diagnostic purposes.

Relative importance and interactions of parameters of finite-element models of human middle ear.

In the last decades, finite-element models of the middle ear have been widely used to predict the middle-ear vibration outputs. Even with the simplest linear assumption for material properties of the structures in the middle ear, these models need tens of parameters. Due to the complexities of measurements of material properties of these structures, accurate estimations of the values of most of these parameters are not possible. In this study, we benefited from the stochastic finite-element model of the middle ear we had developed in the past, to perform global sensitivity analysis. For this aim, we implemented Sobol' sensitivity analysis which ranks the importance of all uncertain parameters and interactions among them at different frequencies. To decrease the computational costs, we found Sobol' indices from surrogate models that we created using stochastic finite-element results and the polynomial chaos expansion method. Based on the results, the Young's modulus and thickness of the tympanic membrane, Young's modulus and damping of the stapedial annular ligaments, and the Young's modulus of ossicles are among the parameters with the greatest impacts on vibrations of the umbo and stapes footplate. Furthermore, the most significant interactions happen between the Young's modulus and thickness of the tympanic membrane.

Inaccuracies of deterministic finite-element models of human middle ear revealed by stochastic modelling.

For over 40 years, finite-element models of the mechanics of the middle ear have been mostly deterministic in nature. Deterministic models do not take into account the effects of inter-individual variabilities on middle-ear parameters. We present a stochastic finite-element model of the human middle ear that uses variability in the model parameters to investigate the uncertainty in the model outputs (umbo, stapes, and tympanic-membrane displacements). We demonstrate: (1) uncertainties in the model parameters can be magnified by more than three times in the umbo and stapes footplate responses at frequencies above 2 kHz; (2) middle-ear models are biased and they distort the output distributions; and (3) with increased frequency, the highly-uncertain regions spatially spread out on the tympanic membrane surface. Our results assert that we should be mindful when using deterministic finite-element middle-ear models for critical tasks such as novel device developments and diagnosis.

Fracture behaviour of human skin in deep needle insertion can be captured using validated cohesive zone finite-element method.

Medical needles have shown an appreciable contribution to the development of novel medical devices and surgical technologies. A better understanding of needle-skin interactions can advance the design of medical needles, modern surgical robots, and haptic devices. This study employed finite element (FE) modelling to explore the effect of different mechanical and geometrical parameters on the needle's force-displacement relationship, the required force for the skin puncture, and generated mechanical stress around the cutting zone. To this end, we established a cohesive FE model, and identified its parameters by a three-stage parameter identification algorithm to closely replicate the experimental data of needle insertion into the human skin available in the literature. We showed that a bilinear cohesive model with initial stiffness of 5000 MPa/mm, failure traction of 2 MPa, and separation length of 1.6 mm can lead to a model that can closely replicate experimental results. The FE results indicated that while the coefficient of friction between the needle and skin substantially changes the needle reaction force, the insertion velocity does not have a noticeable effect on the reaction force. Regarding the geometrical parameters, needle cutting angle is the prominent factor in terms of stress fields generated in the skin tissue. However, the needle diameter is more influential on the needle reaction force. We also presented an energy study on the frictional dissipation, damage dissipation, and strain energy throughout the insertion process.

Material characterization of thin planar structures using full-field harmonic vibration response measured with stroboscopic holography.

We propose a novel material characterization method to estimate the Young's modulus of thin 2-D structures using non-modal noisy single frequency harmonic vibration data measured with holography. The method uses finite-difference discretization to apply the plate equation to all measured pixels inside the boundary of the vibrating structure and then treats the problem as a Bayesian optimization process to find the value of the Young's modulus by minimizing the Euclidian distance between the measured displacement field and repeatedly calculated displacement field using the plate equation. In order to assess the accuracy of the method, ground truth harmonic displacement magnitude fields of different plates were obtained using analytical solutions and the finite-element method and were used to estimate the Young's moduli. We applied Gaussian and non-Gaussian noise with different intensities to assess the robustness and accuracy of the proposed material characterization method in the presence of noise. We demonstrated that for multiple benchmarks for signal to noise ratio of down to 0 dB, our proposed method had errors of less than 5%. We also quantified the effects of uncertainties in the geometrical and material parameters as well as boundary conditions on the estimated Young's modulus. Furthermore, we studied the effects of the mesh size on the runtime and applied the method to experimental holography vibration measurement data of a copper plate.