Overview talk

Round window (RW) stimulation is an application of active middle ear implants. However, its clinical outcomes show a substantial degree of variability due to the mismatch of the diameter of the actuator and the RW membrane, and the surgically challenging for placing the actuator. To address this problem, we proposed a new piezoelectric actuator for RW stimulation. Firstly, we designed the actuator’s mechanical structure specifically for the RW stimulation. Then, a finite element (FE) model of the piezoelectric actuator coupled to the human ear’s RW membrane was constructed. Based on this coupled FE model, the influence of the actuator’s supporting stiffness and piezoelectric stack layer number on the stimulated stapes velocity were studied. Finally, the piezoelectric actuator was fabricated and its output was tested on human cadaver temporal bones. The results show that the developed piezoelectric actuator has the advantages of a wide operating frequency range and controlled preload on the RW membrane.

Machine learning algorithms and neural networks have recently been used for the classification of middle ear disorders using Wideband acoustic immittance and wideband tympanometry data. This study applies the extreme gradient boosting (XGB) classifier, trained on simulated WAI data, to classify real measured data for normal, otosclerotic, and disarticulated ears. The interpretability methods SHAP and LIME are used to quantify each feature’s contribution, both revealing energy reflectance between 600-800 Hz as a key feature for all classes. The key feature identified matches the differences that can be visually observed in the training and test data. However, the obtained feature contributions don’t provide enough distinguishable information to recognise incorrect or uncertain classifications.

Introduction:

Nowadays, human temporal bone preparations are commonly used as models for testing implants and validating numerical results, as their properties are close to those of living humans. Measurements on cadaveric preparations are affected by large measurement uncertainties, which is why the use of technical models as reproducible test environments have significant advantages. Various research groups have pursued this goal, but so far no adequate model of the middle-ear could be achieved, as the mechanical-dynamic properties of the tympanic membrane, ligaments and joints cannot be realized using conventional 3D-printing materials. The objective of the present work is to develop a multi-material system using additive manufacturing, which consists of different proportions of different photopolymer materials and whose dynamic-mechanical property profile corresponds realistically to that of human ligaments.

Methods:

Using 3D-Inkjet technology, homogeneously distributed multi-material substrates are produced. The mechanical-dynamic properties of these test substrates are analyzed in vibration measurements and tensile tests. By a voxel-based definition of systematically varied separate photopolymers, whose individual property profiles range from almost ideal elastic to highly viscous behavior, a dynamic-mechanical property profile analogous to that of human middle ear ligaments is to be achieved.

Results:

It was found that the material properties of the multi-material substrates can be specifically influenced by systematically adding three photopolymers (Tissue Matrix, Vero, Agilus). Densities from 1100 to 1180 kg/m³were achieved, Young’s modulus from 0.1 to 25 MPa and Poisson’s ratio of around 0.3. Target values for human ligaments reported in the literature vary, with density values ranging from 1100 to 1200 kg/m³, Young’s modulus from 0.05 to 21 MPa, damping ratio from 0.003 to 0.05, and Poisson’s ratio of around 0.3.

Conclusion:

The use of the 3D-Inkjet methode to produce human ligaments enables a realistic representation of the linear, dynamic-mechanical properties. With multi-material printing, up to eight materials can be processed simultaneously, which opens up further possibilities in terms of non-linear properties. The technology offers the potential for the further development of technical-models of the middle-ear.

In this study a method for determining the best placement and optimal orientation of the piezoelectric accelerometer attached to the short process of the incus is developed. The best location is determined to be near the incudomalleolar joint. The optimal orientation of the sensor at the determined location is obtained using two criteria – maximum voltage sum and minimum loudness level sum. Both criteria result in the orientation of the accelerometer where z-axis points towards the stapes’ footplate and y-axis points along the short process of the incus towards the its posterior ligament. This method can be used to further optimize the design and the performance of the accelerometer. The influence of the accelerometer’s mass and its connection to the incus should be investigated in more detail.