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Estimation method of dynamic range parameters for cochlear implants based on neural response telemetry threshold.

Background: There is a lack of correlation studies between subjective behavioral test threshold and neural response telemetry (NRT) thresholds in cochlear implant (CI) patients. At present, there is no predictive model that can predict the parameters of CI adjustment objectively and reliably. Object...

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Published in:Acta Oto-Laryngologica Vol. 145; no. 7; pp. 618 - 627
Main Authors: Liu, Yao, Wang, Yue, Chen, Yu, Wang, Wei
Format: research tables/charts Journal Article
Published: Taylor & Francis Ltd Jul2025
Online Access:View this record in EBSCOhost
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      dt: Jul2025
      vid: 145
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/00016489.2025.2492359
        186450338
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        atl: Estimation method of dynamic range parameters for cochlear implants based on neural response telemetry threshold.
      aug:
        au:
          Liu, Yao
          Wang, Yue
          Chen, Yu
          Wang, Wei
        affil: Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
      sug:
        subj:
          Telemetry
          Auditory Threshold
          Cochlear Implant
          Cochlea Anatomy and Histology
          Ear, Inner Abnormalities
          Deep Learning
          Cochlear Implant Programming
          Evoked Potentials, Auditory
          Human
          Male
          Female
          Correlational Studies
          Descriptive Statistics
          Audiometry
          Convolutional Neural Networks
          Intraoperative Monitoring
          Auditory Perception
          Hearing Tests
          Funding Source
          Male
          Female
      ab:
        Background: There is a lack of correlation studies between subjective behavioral test threshold and neural response telemetry (NRT) thresholds in cochlear implant (CI) patients. At present, there is no predictive model that can predict the parameters of CI adjustment objectively and reliably. Objectives: To explore the correlation between the subjective behavior test method threshold and the NRT thresholds in patients with CI with normal cochlear (NC) morphology and inner ear malformation (IEM). To explore the value of using deep learning technology to predict the parameters of machine adjustment and guide the postoperative machine adjustment. Methods: NRT and subjective behavior tests were conducted on 57 cases of CI patients with NC morphology and 20 cases of IEM using electrodes 1, 6, 11, 16, and 22, respectively. The correlation between the NRT thresholds and T and C values was analyzed. Using deep learning techniques, establish a prediction model based on convolutional neural networks to predict the parameters of machine adjustment of CI. Results: The average NRT thresholds values of the NC group and the IEM group were both greater than the T values, close to and slightly smaller than the C values. The average values of T values, C values, and NRT thresholds in the IEM group were slightly higher than those in the NC group. The NRT thresholds of the both groups is significantly correlated with the C values and T values. The constructed prediction model has high accuracy between the predicted values and the actual values of each electrode. Moreover, the linear regression equation between the predicted and actual values is highly similar. Conclusions: The NRT thresholds is significantly related to the subjective behavior test threshold. The correlation between NRT thresholds and T or C values can be used to assist in CI tuning. Especially for patients with IEM, different machine adjustment strategies should be adopted compared to NC patients. Moreover, the constructed neural network prediction model can also guide the postoperative adjustment of patients with cochlear implants.
        缺乏人工耳蜗 (CI) 患者主观行为测试阈值与神经反应遥测 (NRT) 阈值之间的相关性研究。目前尚无能够客观可靠地预测 CI 调节参数的预测模型。 探讨耳蜗形态正常且内耳畸形的 CI 患者主观行为测试方法阈值与 NRT 阈值之间的相关性, 探索深度学习技术在预测机器调节参数, 并指导术后机器调节上的价值。 对 57 例耳蜗形态正常的人工耳蜗患者和 20 例内耳畸形患者分别使用 1、6、11、16 和 22 号电极进行 NRT 和主观行为测试, 并分析 NRT 阈值与 T 值和 C 值的相关性。使用深度学习技术, 建立基于卷积神经网络的预测模型, 用于预测人工耳蜗的机器调节参数。 耳蜗正常组和内耳畸形组的平均NRT阈值均大于T值, 接近或略小于C值。内耳畸形组的T值、C值和NRT阈值的平均值均略高于耳蜗正常组。两组的NRT阈值与C值和T值均呈显著相关性。所构建的预测模型在预测值与各电极实际值之间具有较高的准确率, 且预测值与实际值之间的线性回归方程高度相似。 NRT阈值与主观行为测试阈值显著相关。NRT阈值与T值或C值的相关性可用于辅助人工耳蜗的机器调节。尤其对于内耳畸形患者, 应采用与正常耳蜗患者不同的机器调整方式。此外, 构建的神经网络预测模型也可以导引人工耳蜗植入患者的术后调整。
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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