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find Author "LIU Houguang" 4 results
  • The effect of preload and support’s stiffness on the performance of round window stimulation: a numerical analysis

    To investigate the influence of the preload and supporting stiffness on the hearing compensation performance of round window stimulation, a coupling finite model composed of a human ear, an actuator and a support was established. This model was constructed based on a complete set of micro-computed tomography (Micro-CT) images of a healthy adult’s right ear by reverse engineering technology. The validity of the model was verified by comparing the model’s calculated results with experimental data. Based on this model, we applied different amplitude preloads on the actuator, and changed the support’s stiffness. Then, the influences of the actuator’s preload and the support’s stiffness were analyzed by comparing the corresponding displacements of the basilar membrane. The results show that after applying a preload on the actuator, its hearing compensation performance was increased at the middle and high frequencies, but was deteriorated at low frequencies; besides, compared with using the fascia as the actuator’s support in clinical practice, utilizing the titanium alloy to fabricate the support would enhance the hearing compensation performance of the round window stimulation in the whole frequency range.

    Release date:2018-04-16 09:57 Export PDF Favorites Scan
  • Numerical analysis of the influence of otitis media on the hearing compensation performance of round-window stimulation

    In order to study the influence of tympanic membrane lesion and ossicular erosion caused by otitis media on the hearing compensation performance of round-window stimulation, a human ear finite element model including cochlear asymmetric structure was established by computed tomography (CT) technique and reverse engineering technique. The reliability of the model was verified by comparing with the published experimental data. Based on this model, the tympanic membrane lesion and ossicular erosion caused by otitis media were simulated by changing the corresponding tissue structure. Besides, these simulated diseases’ effects on the round-window stimulation were studied by comparing the corresponding basilar-membrane’s displacement at the frequency-dependent characteristic position. The results show that the thickening and the hardening of the tympanic membrane mainly deteriorated the hearing compensation performance of round-window stimulation in the low frequency; tympanic membrane perforation and the minor erosion of ossicle with ossicular chain connected slightly effected the hearing compensation performance of round-window stimulation. Whereas, different from the influence of the aforementioned lesions, the ossicular erosion involving the ossicular chain detachment increased its influence on performance of round-window stimulation at the low frequency. Therefore, the effect of otitis media on the hearing compensation performance of round-window stimulation should be considered comprehensively when designing its actuator, especially the low-frequency deterioration caused by the thickening and the hardening of the tympanic membrane; the actuator’s low-frequency output should be enhanced accordingly to ensure its postoperative hearing compensation performance.

    Release date:2019-12-17 10:44 Export PDF Favorites Scan
  • Numerical study on the effect of middle ear malformations on energy absorbance

    In order to study the effect of middle ear malformations on energy absorbance, we constructed a mechanical model that can simulate the energy absorbance of the human ear based on our previous human ear finite element model. The validation of this model was confirmed by two sets of experimental data. Based on this model, three common types of middle ear malformations, i.e. incudostapedial joint defect, incus fixation and malleus fixation, and stapes fixation, were simulated by changing the structure and material properties of the corresponding tissue. Then, the effect of these three common types of middle ear malformations on energy absorbance was investigated by comparing the corresponding energy absorbance. The results showed that the incudostapedial joint defect significantly increased the energy absorbance near 1 000 Hz. The incus fixation and malleus fixation dramatically reduced the energy absorbance in the low frequency, which made the energy absorbance less than 10% at frequencies lower than 1 000 Hz. At the same time, the peak of energy absorbance shifted to the higher frequency. These two kinds of middle ear malformations had obvious characteristics in the wideband acoustic immittance test. In contrast, the stapes fixation only reduced the energy absorbance in the low frequency and increased energy absorbance in the middle frequency slightly, which had no obvious characteristic in the wideband acoustic immittance test. These results provide a theoretical reference for the wideband acoustic immittance diagnosis of middle ear malformations in clinic.

    Release date:2021-04-21 04:23 Export PDF Favorites Scan
  • Research on computer aided diagnosis of otitis media based on faster region convolutional neural network

    Otitis media is one of the common ear diseases, and its accurate diagnosis can prevent the deterioration of conductive hearing loss and avoid the overuse of antibiotics. At present, the diagnosis of otitis media mainly relies on the doctor's visual inspection based on the images fed back by the otoscope equipment. Due to the quality of otoscope equipment pictures and the doctor's diagnosis experience, this subjective examination has a relatively high rate of misdiagnosis. In response to this problem, this paper proposes the use of faster region convolutional neural networks to analyze clinically collected digital otoscope pictures. First, through image data enhancement and preprocessing, the number of samples in the clinical otoscope dataset was expanded. Then, according to the characteristics of the otoscope picture, the convolutional neural network was selected for feature extraction, and the feature pyramid network was added for multi-scale feature extraction to enhance the detection ability. Finally, a faster region convolutional neural network with anchor size optimization and hyperparameter adjustment was used for identification, and the effectiveness of the method was tested through a randomly selected test set. The results showed that the overall recognition accuracy of otoscope pictures in the test samples reached 91.43%. The above studies show that the proposed method effectively improves the accuracy of otoscope picture classification, and is expected to assist clinical diagnosis.

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