PURPOSE:Cartilage is widely used for tympanic membrane (TM) reconstruction, but the impact of graft position relative to the bony ear canal on middle ear mechanics remains unclear. This study examined how cartilage placement affects the middle ear transfer function (METF) and TM vibration. METHODS:Single‑point Laser Doppler Vibrometry (LDV) was performed on five fresh frozen human temporal bones before and after creating a 2.5 mm perforation in the posterior inferior TM quadrant and after reconstruction with a round-shaped cartilage graft of 4 mm diameter and 0.5 mm thickness placed either on (on‑bony) or separated from (off‑bony) the bony ear canal. TM motion was further assessed using a scanning LDV in a technical membrane model. A validated finite element (FE) model of the middle ear was used to replicate temporal bone measurements and the effect of an experimentally opened versus closed tympanic cavity, representing the in-vivo condition in patients, was evaluated by FE modelling. RESULTS:After TM perforation, the METF significantly decreased between 562 - 2094 Hz, and 2951 - 3194 Hz, while showing increased measurement variability below 500 Hz. When performing reconstruction with either the on-bony or the off-bony technique, the METFs partially recover, but show a significant difference in METF compared to the intact TM in the frequency range of 146 - 195, 200 - 217, 739 - 933 Hz (on-bony) and 146 - 295, 718 - 930, 722 - 930 and 1332 - 1719 Hz (off-bony). No significant difference in METF between the on-bony and off-bony reconstruction were found (p > 0.05). Scanning LDV analysis of a technical membrane model showed similar membrane peak velocities between the two graft positions, although the off‑bony configuration shifted the best frequency to lower values. FE simulations of the middle ear confirmed the findings of the temporal bone measurements. Additionally, the model work showed that middle ear transmission with an experimentally opened tympanic cavity differed by <2 dB after on- or off-bony reconstruction compared to a closed tympanic cavity. This was different for the perforated TM: While the transmission loss was nearly constant with an experimentally opened tympanic cavity, a closed tympanic cavity produced frequency‑dependent transmission loss exceeding 40 dB. CONCLUSION:In this temporal bone study, placing the cartilage on the bony ear canal wall did not have an impact on the METF. FE modeling supported the temporal bone findings and suggested that similar behavior could be expected in vivo with a closed tympanic cavity. However, the FE-model suggests that perforation‑induced losses were substantially underestimated in the temporal bone measurements with an experimentally opened tympanic cavity. Positioning of the cartilage graft on the bony ear canal can be advantageous in patients with chronic middle-ear disease to reduce the risk of medialization and formation of retraction pockets without compromising the patients hearing outcome.

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 achieved macro recall of 82 % is comparable to other approaches trained with real measurement data. The interpretability methods LIME and SHAP 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.

Current noninvasive methods of clinical practice often do not identify the causes of conductive hearing loss due to pathologic changes in the middle ear with sufficient certainty. Wideband acoustic immittance (WAI) measurement is noninvasive, inexpensive and objective. It is very sensitive to pathologic changes in the middle ear and therefore promising for diagnosis. However, evaluation of the data is difficult because of large interindividual variations. Machine learning methods like Convolutional neural networks (CNN) which might be able to deal with this overlaying pattern require a large amount of labeled measurement data for training and validation. This is difficult to provide given the low prevalence of many middle-ear pathologies. Therefore, this study proposes an approach in which the WAI training data of the CNN are simulated with a finite-element ear model and the Monte-Carlo method. With this approach, virtual populations of normal, otosclerotic, and disarticulated ears were generated, consistent with the averaged data of measured populations and well representing the qualitative characteristics of individuals. The CNN trained with the virtual data achieved for otosclerosis an AUC of 91.1 %, a sensitivity of 85.7 %, and a specificity of 85.2 %. For disarticulation, an AUC of 99.5 %, sensitivity of 100 %, and specificity of 93.1 % was achieved. Furthermore, it was estimated that specificity could potentially be increased to about 99 % in both pathological cases if stapes reflex threshold measurements were used to confirm the diagnosis. Thus, the procedures’ performance is comparable to classifiers from other studies trained with real measurement data, and therefore the procedure offers great potential for the diagnosis of rare pathologies or early-stages pathologies. The clinical potential of these preliminary results remains to be evaluated on more measurement data and additional pathologies.

Background: Wideband acoustic immittance (WAI) and wideband tympanometry (WBT) are promising approaches to improve diagnosis accuracy in middle-ear diagnosis, though due to significant interindividual difference, their analysis and interpretation remains challenging. Recent approaches have come up, implementing machine learning (ML) or deep learning classifiers trained with measured WAI or WBT data for the classification of otitis media or otosclerosis. Also, first approaches have been made in identifying important regions from the WBT data, which the classifiers used for their decision-making. Methods: Two classifiers, a convolutional neural network (CNN) and the ML algorithm extreme gradient boosting (XGB), are trained on artificial data obtained with a finite-element ear model providing the middleear measurements energy reflectance (ER), pressure reflectance phase, impedance amplitude and phase. The performance of both classifiers is evaluated by cross-validation on artificial test data and by classification of real measurement data from the literature using the metrics macro-recall and macro-F1 score. The feature contributions are quantified using the feature importance 'gain' for XGB and deep Taylor decomposition for CNN. Results: In the cross-validation with artificial data, the macro-recall and macro-F1 scores are similar, namely 91.2% for XGB and 94.5% for CNN. For the classification with real measurement data the macrorecall and macro-F1-score were 81.8% and 38.2% (XGB) and 81.0% and 54.8% (CNN), respectively. The key features identified are ER between 600-1,000 Hz together with impedance phase between 600-1,000 Hz for XGB and ER up to 1,500 Hz for CNN. Conclusions: We were able to show that the applied classifiers CNN and XGB trained with simulated data lead to a reasonably well performance on real data. We conclude that using simulation-based WAI data can be a successful strategy for classifier training and that XGB can be applied to WAI data. Furthermore, ML interpretability algorithms are useful to identify relevant key features for differential diagnosis and to increase confidence in classifier decisions. Further evaluation using more measured data, especially for pathological cases, is essential.

Injury or inflammation of the middle ear often results in the persistent tympanic membrane (TM) perforations, leading to conductive hearing loss (HL). However, in some cases the magnitude of HL exceeds that attributable by the TM perforation alone. The aim of the study is to better understand the effects of location and size of TM perforations on the sound transmission properties of the middle ear. The middle ear transfer functions (METF) of six human temporal bones (TB) were compared before and after perforating the TM at different locations (anterior or posterior lower quadrant) and to different degrees (1 mm, ¼ of the TM, ½ of the TM, and full ablation). The sound-induced velocity of the stapes footplate was measured using single-point laser-Doppler-vibrometry (LDV). The METF were correlated with a Finite Element (FE) model of the middle ear, in which similar alterations were simulated. The measured and calculated METF showed frequency and perforation size dependent losses at all perforation locations. Starting at low frequencies, the loss expanded to higher frequencies with increased perforation size. In direct comparison, posterior TM perforations affected the transmission properties to a larger degree than anterior perforations. The asymmetry of the TM causes the malleus-incus complex to rotate and results in larger deflections in the posterior TM quadrants than in the anterior TM quadrants. Simulations in the FE model with a sealed cavity show that small perforations lead to a decrease in TM rigidity and thus to an increase in oscillation amplitude of the TM mainly above 1 kHz. Size and location of TM perforations have a characteristic influence on the METF. The correlation of the experimental LDV measurements with an FE model contributes to a better understanding of the pathologic mechanisms of middle-ear diseases. If small perforations with significant HL are observed in daily clinical practice, additional middle ear pathologies should be considered. Further investigations on the loss of TM pretension due to perforations may be informative.

The incudo-malleal joint (IMJ) in the human middle ear is a true diarthrodial joint and it has been known that the flexibility of this joint does not contribute to better middle-ear sound transmission. Previous studies have proposed that a gliding motion between the malleus and the incus at this joint prevents the transmission of large displacements of the malleus to the incus and stapes and thus contributes to the protection of the inner ear as an immediate response against large static pressure changes. However, dynamic behavior of this joint under static pressure changes has not been fully revealed. In this study, effects of the flexibility of the IMJ on middle-ear sound transmission under static pressure difference between the middle-ear cavity and the environment were investigated. Experiments were performed in human cadaveric temporal bones with static pressures in the range of +/- 2 kPa being applied to the ear canal (relative to middle-ear cavity). Vibrational motions of the umbo and the stapes footplate center in response to acoustic stimulation (0.2-8 kHz) were measured using a 3D-Laser Doppler vibrometer for (1) the natural IMJ and (2) the IMJ with experimentally-reduced flexibility. With the natural condition of the IMJ, vibrations of the umbo and the stapes footplate center under static pressure loads were attenuated at low frequencies below the middle-ear resonance frequency as observed in previous studies. After the flexibility of the IMJ was reduced, additional attenuations of vibrational motion were observed for the umbo under positive static pressures in the ear canal (EC) and the stapes footplate center under both positive and negative static EC pressures. The additional attenuation of vibration reached 4~7 dB for the umbo under positive static EC pressures and the stapes footplate center under negative EC pressures, and 7~11 dB for the stapes footplate center under positive EC pressures. The results of this study indicate an adaptive mechanism of the flexible IMJ in the human middle ear to changes of static EC pressure by reducing the attenuation of the middle-ear sound transmission. Such results are expected to be used for diagnosis of the IMJ stiffening and to be applied to design of middle-ear prostheses.

Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is, therefore, fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation, and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics and address the challenges of limited measurement data and wide parameter ranges from the literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

This study describes a non-contact measuring and system identification procedure for evaluating inhomogeneous stiffness and damping characteristics of the annular ligament in the physiological amplitude and frequency range without the application of large static external forces that can cause unnatural displacements of the stapes. To verify the procedure, measurements were first conducted on a steel beam. Then, measurements on an individual human cadaveric temporal bone sample were performed. The estimated results support the inhomogeneous stiffness and damping distribution of the annular ligament and are in a good agreement with the multiphoton microscopy results which show that the posterior-inferior corner of the stapes footplate is the stiffest region of the annular ligament.

Today's audiometric methods for the diagnosis of middle ear disease are often based on a comparison of measurements with standard curves, that represent the statistical range of normal hearing responses. Because of large inter-individual variances in the middle ear, especially in wideband tympanometry (WBT), specificity and quantitative evaluation are greatly restricted. A new model-based approach could transform today's predominantly qualitative hearing diagnostics into a quantitative and tailored, patient-specific diagnosis, by evaluating WBT measurements with the aid of a middle-ear model. For this particular investigation, a finite element model of a human ear was used. It consisted of an acoustic ear canal and a tympanic cavity model, a middle-ear with detailed nonlinear models of the tympanic membrane and annular ligament, and a simplified inner-ear model. This model has made it possible for us to simulate pathologies like the stiffening of ligaments or joints, because we can simply change the corresponding mechanical parameters of the model. On the other hand, it is also possible to identify pathologies from measurements, by analyzing the parameters obtained by a system identification procedure. This reduces the number of required model parameters through sensitivity studies and parameter clustering. Uncertainties due to the lack of knowledge, subjectivity in numerical implementation and model simplification are taken into account by the application of fuzzy arithmetic. The most confident parameter set can be determined by applying an inverse fuzzy method on the measurement data. The principle and the benefits of this model-based approach are illustrated by the example of a two-mass oscillator, and also by the simulation of the energy absorbance of an ear with malleus fixation, where the parameter changes that are introduced can be determined quantitatively through the system identification.

This study simulates acoustic impedance measurements in the human ear canal and investigates error influences due to improperly accounted evanescence in the probe’s near field, cross-section area changes, curvature of the ear canal, and pressure inhomogeneities across the tympanic membrane, which arise mainly at frequencies above 10 kHz. Evanescence results from strongly damped modes of higher order, which can only be found in the near field of the sound source and are excited due to sharp cross-sectional changes as they occur at the transition from the probe loudspeaker to the ear canal. This means that different impedances are measured depending on the probe design. The influence of evanescence cannot be eliminated completely from measurements, however, it can be reduced by a probe design with larger distance between speaker and microphone. A completely different approach to account for the influence of evanescence is to evaluate impedance measurements with the help of a finite element model, which takes the precise arrangement of microphone and speaker in the measurement into account. The latter is shown in this study exemplary on impedance measurements at a tube terminated with a steel plate. Furthermore, the influences of shape changes of the tympanic membrane and ear canal curvature on impedance are investigated.