25 research outputs found

    Middle ear anatomy and implant sizes: correlates and the need for uniform implant dimensions

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    IntroductionConductive hearing loss describes an insufficient sound transfer of the middle ear, often caused by defects or absence of the ossicles. Depending on the specific middle ear dimensions and the kind of defect, surgeons can choose from a variety of passive implants to reconstruct the middle ear and hence restore sound transmission. However, the latter is only achieved if the optimal implant size is available and selected for each individual patient.MethodsAnatomical dimensions relevant for middle ear reconstruction were assessed within high-resolution clinical imaging data of 50 patients (100 ears). The ranges of these dimensions were then compared to implant types and sizes available from different manufacturers.ResultsIn general, total and partial prostheses seem to cover the whole range of anatomical variations. A lack of stapesplasty implants was found for particularly small anatomies. Various implant lengths of all types far exceed dimensions necessary for successful restoration of sound transmission. In some cases, implant lengths are not clearly specified by the manufacturer. Tympanic membrane and stapes axis were not in line for any of the investigated middle ears.ConclusionClear specifications of implant lengths are crucial to allow for successful hearing restoration, and clinics often need to have more than one implant type to cover the entire range of anatomical variations they may encounter. There appears to be an unmet clinical need for smaller stapesplasty implants. Devices which allow for an angular adjustment between distal and proximal end appear to mimic the orientation of the ossicles more naturally

    Validation of a Cochlear Implant Patient Specific Model of the Voltage Distribution in a Clinical Setting

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    Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large inter-subject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have been also parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: A homogeneous model (HM), a non-patient specific model (NPSM) and a patient specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient specific geometry and electrode positions we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs

    Data evaluation snap.

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    Example to demonstrate that calculation of snap is independent from insertion speed in contrast to value for jerk. (DOCX)</p

    Phantom orientation and test setup.

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    For the orientation of the ST model (a) the CCS [52] is adopted with the y-axis being the insertion axis. The model is transferred to the physical insertion phantom (b) and an idealized cochlea opening is created. The model is placed in the test setup (c) for CI electrode array insertion. To control the boundary conditions and create a steady state during insertion, the electrode array is inserted through a guide tube (d) into the phantom. Electrode insertion starts at EID4 = 4mm = ystart (e).</p

    Scala tympani mean insertion phantom.

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    Transformation from the cochlear coordinate system to newly defined insertion coordinate system. (DOCX)</p

    Insertion force profile of the three FLEX28 electrodes.

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    Three electrodes in (a)-(c); v = 0.5 mm/s, lubrication 90% soap solution. Qualitatively only the very first insertion of each electrode appears to differ from the subsequent and last (ins. 21) insertion. Differences between the electrodes appear to become negligible after the conditioning cycle (d).</p

    Parametrization of mean insertion phantom.

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    The parameters AST (basal ST diameter, measured from the center of the round window through the modiolus to the opposite wall of the ST) and BST (basal turn width of the ST orthogonal to AST) describe the dimensions of the basal turn of the mean ST. HST (distance from the lowest to the highest point f the ST lumen along z) describes its overall height and HS, ST the height of its lateral wall (LW) spiral [49]. The Cochlear duct lengths CDLLW,ST and CDLST are measured along the LW of the phantom from the center of the round window to the most apical point and express the corresponding length as metric and angular length respectively [55]. The parameter “Area cochleostomy” describes the surface area of the basal ST phantom opening for CI array insertion.</p

    Scala Tympani (ST) model generation.

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    Generation of the ST model follows several steps to preserve the common anatomical features of the cross-sectional geometry [51], as this defines the contact area between electrode and phantom. Cross sections can vary in size and orientation between individuals (a,b) [3, 34] and were rotated to the cochlear angle dependent average orientation of the basilar membrane (c). Then manual segmentations points were redistributed evenly and consistent for all cross sections of the n = 15 datasets and datapoints were averaged at the respective angular location (d).</p
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