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Related Experiment Video

Updated: May 24, 2026

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion
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Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

A Data-Driven Visit Windowing Approach Applied to Cochlear Implant Follow-Up Data.

Annette Günther1, Oliver J Bott2, Eugen Kludt1

  • 1Department of Otolaryngology, Hannover Medical School, Hannover, Germany.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

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A new data-driven visit windowing (DDW) method improves cochlear implant (CI) follow-up analysis by better accounting for real-world visit timing. This approach enhances data consistency and prediction accuracy for hearing outcomes.

Area of Science:

  • Audiology
  • Medical Data Analysis
  • Biostatistics

Background:

  • Cochlear implant (CI) patients need consistent follow-up for optimal hearing.
  • Clinical visits often deviate from schedules, impacting data reliability.
  • Existing study-protocol visit window (SPW) methods lack flexibility.

Purpose of the Study:

  • To develop and evaluate a data-driven visit windowing (DDW) approach for CI patient follow-up.
  • To compare the DDW method against the traditional SPW scheme.
  • To improve the reproducibility and comparability of longitudinal CI data analysis.

Main Methods:

  • Analyzed 31,344 speech test visits from 5,264 CI patients.
  • Defined data-driven visit windows with start/end points.
Keywords:
clinical datacochlear implantsdata-driven analysismachine learningreproducibilityspeech recognitiontime seriesvisit windowing

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Last Updated: May 24, 2026

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  • Developed a rule to select the most representative visit within each window.

    • Main Results:

    • DDW windows are reproducible and capture visit timing variability.
    • DDW covered 40.3% more visits than SPW.
    • DDW achieved slightly lower prediction errors for speech recognition.

    Conclusions:

    • DDW offers a reproducible, extensible, and clinically relevant basis for defining visits.
    • Enables consistent longitudinal CI analyses and improved data quality.
    • Facilitates better data comparisons across different clinical centers and studies.