An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning

Schuerch, Klaus; Wimmer, Wilhelm; Dalbert, Adrian; Rummel, Christian; Caversaccio, Marco; Mantokoudis, Georgios; Gawliczek, Tom; Weder, Stefan

doi:10.1038/s41597-023-02055-9

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…According to Rasetshwane et al ( 20 ), the subjects categorized the loudness of the acoustic ECochG stimuli into seven categories (not audible, very soft, soft, medium, loud, very loud, and too loud). The evaluated dataset is available at Schuerch et al ( 21 ).…”

Section: Methodsmentioning

confidence: 99%

Objective evaluation of intracochlear electrocochleography: repeatability, thresholds, and tonotopic patterns

et al. 2023

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IntroductionIntracochlear electrocochleography (ECochG) is increasingly being used to measure residual inner ear function in cochlear implant (CI) recipients. ECochG signals reflect the state of the inner ear and can be measured during implantation and post-operatively. The aim of our study was to apply an objective deep learning (DL)-based algorithm to assess the reproducibility of longitudinally recorded ECochG signals, compare them with audiometric hearing thresholds, and identify signal patterns and tonotopic behavior.MethodsWe used a previously published objective DL-based algorithm to evaluate post-operative intracochlear ECochG signals collected from 21 ears. The same measurement protocol was repeated three times over 3 months. Additionally, we measured the pure-tone thresholds and subjective loudness estimates for correlation with the objectively detected ECochG signals. Recordings were made on at least four electrodes at three intensity levels. We extracted the electrode positions from computed tomography (CT) scans and used this information to evaluate the tonotopic characteristics of the ECochG responses.ResultsThe objectively detected ECochG signals exhibited substantial repeatability over a 3-month period (bias-adjusted kappa, 0.68; accuracy 83.8%). Additionally, we observed a moderate-to-strong dependence of the ECochG thresholds on audiometric and subjective hearing levels. Using radiographically determined tonotopic measurement positions, we observed a tendency for tonotopic allocation with a large variance. Furthermore, maximum ECochG amplitudes exhibited a substantial basal shift. Regarding maximal amplitude patterns, most subjects exhibited a flat pattern with amplitudes evenly distributed over the electrode carrier. At higher stimulation frequencies, we observed a shift in the maximum amplitudes toward the basal turn of the cochlea.ConclusionsWe successfully implemented an objective DL-based algorithm for evaluating post-operative intracochlear ECochG recordings. We can only evaluate and compare ECochG recordings systematically and independently from experts with an objective analysis. Our results help to identify signal patterns and create a better understanding of the inner ear function with the electrode in place. In the next step, the algorithm can be applied to intra-operative measurements.

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Section: Methodsmentioning

confidence: 99%

Objective evaluation of intracochlear electrocochleography: repeatability, thresholds, and tonotopic patterns

et al. 2023

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“…While intracochlear ECochG provides valuable information on the tonotopic organization of the cochlea, patients undergoing CI can have inner ear pathology resulting in ECochG nonresponsiveness above the noise floor. 24,36 This variability may explain why nonrobust and nontonotopic ECochG patterns can be observed even when the internal array is well-positioned on postinsertion imaging. Further studies are needed to better understand the pathophysiology of tonotopic changes in sensorineural hearing loss and to characterize the different patterns of intracochlear ECochG responses.…”

Section: Nonrobust and Nontonotopic Ecochg With A Properly Inserted Smementioning

confidence: 99%

Identifying Slim Modiolar Electrode Tip Fold‐Over With Intracochlear Electrocochleography

Varghese,

Walia,

Lefler

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo evaluate the predictive value of intracochlear electrocochleography (ECochG) for identifying tip fold‐over during cochlear implantation (CI) using the slim modiolar electrode (SME) array.Study DesignProspective cohort study.SettingTertiary referral center.MethodsFrom July 2022 to June 2023, 142 patients, including adults and children, underwent intracochlear ECochG monitoring during and after SME placement. Tone‐bursts were presented from 250 Hz to 2 kHz at 108 to 114 dB HL. A fast Fourier transform (FFT) allowed for frequency‐specific evaluation of ECochG response. ECochG patterns during insertion and postinsertion were evaluated using sensitivity and specificity analysis to predict tip fold‐over. Intraoperative plain radiographs served as a reference standard.ResultsFifteen tip fold‐over cases occurred (10.6%) with significant ECochG response (>2 µV). Sixty‐one cases without tip fold‐over occurred (43.0%) with significant ECochG response. All tip fold‐overs had both a nontonotopic postinsertion sweep and nonrobust active insertion pattern. No patients with robust insertion or tonotopic sweep patterns had tip fold‐over. Sensitivity of detecting tip fold‐over when having both nonrobust insertion and nontonotopic sweep patterns was 100% (95% confidence inteval [CI] 78.2%‐100%), specificity was 68.9% (95% CI 55.7%‐80.1%), and the overall accuracy was 72.0% (95% CI 60.5%‐81.7%).ConclusionIntracochlear ECochG monitoring during cochlear implantation with the SME can be a valuable tool for identifying properly positioned electrode arrays. In cases where ECochG patterns are nonrobust on insertion and nontonotopic for electrode sweeps, there may be a concern for tip fold‐over, and intraoperative imaging is necessary to confirm proper insertion.

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“…AI has been used to develop prediction models based on the analysis of existing data sets to predict cancer progression, 29 overall survival stratification, 30 lymph node metastases, 31,32 treatment‐related toxicity 11 and hospital stay length 33 . In otorhinolaryngology, AI has potential applications in audiology, 34,35 otology, 36,37 rhinology 38 and obstructive sleep apnoea 39 . We summarized potential practical applications of AI in fields associated with head and neck surgery in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

Wojtera,

Szewczyk,

Pieńkowski

et al. 2024

Journal of Surgical Oncology

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Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

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An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning

Cited by 7 publications

References 51 publications

Objective evaluation of intracochlear electrocochleography: repeatability, thresholds, and tonotopic patterns

Objective evaluation of intracochlear electrocochleography: repeatability, thresholds, and tonotopic patterns

Identifying Slim Modiolar Electrode Tip Fold‐Over With Intracochlear Electrocochleography

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

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