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Home Professor Craig A. Buchman – Washington University School of Medicine

    Craig A Buchman

    Head, Otolaryngology – Head & Neck Surgery and Professor of Otolaryngology
    Washington University School of Medicine

    Contributor's Details

    Phone: +1 314 362 7667
    Email: buchmanc@wustl.edu
    Website: Visit Website
    Twitter: Follow on Twitter

    Contributor's Articles

    • Figure 1: After controlling for cochlear health (assessed via RW-ECochG-TR), our data reveal that a short lateral wall electrode is linked to poorer CI performance when residual hearing is not preserved. Conversely, when a short lateral wall electrode is used, and residual hearing is preserved – enabling the use of electro-acoustic stimulation (EAS) – patients achieve excellent performance, whereas loss of residual hearing leads to significant underperformance.
      Optimizing outcomes: The role of surgical technique and intraoperative factors in cochlear implant performance
    • Figure 1. Comparison of prediction models in A) two-dimensional (2D) and B) three-dimensional (3D) analyses. The 2D model demonstrates a poor-fitting linear relationship constrained by limited variables. In contrast, the 3D model incorporates an additional dimension, providing a better fit and improved predictive accuracy. This highlights how multi-dimensional analysis, such as those enabled by machine learning, can uncover more complex relationships within heterogeneous clinical and biological data.
      Predicting cochlear implant performance: Moving beyond single biomarkers and leveraging artificial intelligence
    • Figure 1: Electrocochleography-total response (ECochG-TR) measured at the round window prior to cochlear implant insertion (RW-ECochG-TR) shows a weak correlation with performance in noise (AzBio +10 dB signal-to-noise ratio). Similarly, the MoCA score, a measure of cognitive function, also exhibits a weak correlation with performance in noise. However, a multivariate model incorporating both cochlear health (ECochG-TR), cognition (MoCA score), and their interaction (product of ECochG-TR and MoCA) explains 46.0% of the variability in noise performance. This finding suggests that while good cochlear health is necessary for strong performance in noise, it is not sufficient on its own – it must be complemented by adequate cognitive function.
      Cognitive function and electrode mapping’s role in cochlear implant performance
    • Figure 1: Box plots of performance measures showing all data points of 250 patients at three months across AzBio in Quiet, AzBio in Background Noise, and CNC in Quiet. All measurements were made with the CI-only condition. There was substantial variability across all speech recognition measures among CI recipients.
      Predicting cochlear implant performance: Impact of demographic, audiologic, surgical factors, and cochlear health
    • Hearing and auditory research for cochlear implant outcomes
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