ArticleAudiology research2026
Automatic Detection of Extracochlear Electrodes in Cochlear Implants Using Electric Field Imaging: Bench and Clinical Validation.
Article in Audiology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesExtracochlear electrodes (EEs), defined as electrode contacts located outside the cochlea, can degrade cochlear implant (CI) performance. We present and validate an Electric Field Imaging (EFI)-based algorithm that helps detect EEs and can be used intra- or post-operatively.
methodsAn algorithm for detecting EEs from EFI data was developed. Validation and testing were performed using three datasets: (a) bench models with known EE conditions (saline and resistive load model), (b) clinical EFI recordings from CI recipients with imaging confirmation (CT or plain X-ray), and (c) EFI recordings without imaging. Primary outcomes included the ability to differentiate extracochlear conditions from fully inserted electrode arrays, as well as the concordance between the number of EE contacts identified by the algorithm and those determined from controlled bench configurations or available imaging data.
resultsThe algorithm reliably differentiated full insertion from EE conditions on bench models and clinical EFI recordings from CI recipients with imaging confirmation. In the imaging-confirmed clinical cohort (6 EE cases among 226 CI recipients), the algorithm achieved 100% sensitivity and specificity. In clinical cases without imaging, the algorithm flagged 3.94% as having extracochlear electrodes, within the range of prevalence reported in the literature. Furthermore, the lateral-wall electrode arrays were associated with a significantly higher extracochlear electrode occurrence compared to the pre-curved arrays.
conclusionsThis EFI-based algorithm may provide a useful screening tool for EEs using routine clinical measurements, supporting intra-operative and post-operative detection while reducing reliance on imaging and thereby aiding clinical decision-making. The method can be integrated into existing clinical fitting software workflow and complements recent work on EFI-based tip fold-over detection. The observed sensitivity and specificity suggest that this approach may provide a valuable tool for identifying EEs in situations where imaging is unavailable or impractical.
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