Hearing research · 2026
A dual-data-driven approach for predicting cochlear implant dynamic range parameters using neural response telemetry and electrically evoked auditory brainstem response thresholds.
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Liu Yao, Yang Yijing, Wang Yue, Chen Yu, Wang Wei
Аннотация
BACKGROUND: Currently, clinical research combining Neural Response Telemetry (NRT) and Electrically Evoked Auditory Brainstem Responses (EABR), for the prediction of cochlear implant (CI) fitting parameters remains scarce.
OBJECTIVES: To investigate the correlations between NRT thresholds, EABR thresholds, and subjective behavioral threshold T/C-levels in CI recipients, and to develop an artificial neural network-based prediction model for CI fitting parameters.
METHODS: NRT thresholds, EABR thresholds, and subjective behavioral threshold T/C-levels across electrodes 1, 6, 11, 16, and 22 were collected from 37 CI recipients, followed by correlation analyses. Using NRT and EABR thresholds as combined input features, a backpropagation (BP) neural network integrated with a Bayesian regularization (BR) mechanism (BR-BP) was constructed to predict the T- and C-levels.
RESULTS: Both NRT and EABR thresholds exhibited significant positive correlations with T- and C-levels (P < 0.05). A three-dimensional linear prediction model directly combining NRT and EABR thresholds failed due to severe multicollinearity, yielding a maximum mean absolute error (MAE) of up to 373.86 CL. In contrast, the BR-BP neural network successfully achieved nonlinear feature decoupling, effectively mitigating the overfitting commonly associated with small sample sizes, and stably constrained the prediction errors across all tested electrodes to within approximately 60 CL.
CONCLUSIONS: NRT and EABR thresholds exhibit significant correlations with subjective behavioral CI fitting parameters. While single-source predictive models may occasionally offer localized accuracy on certain electrodes, their vulnerability to signal loss limits their clinical safety. The proposed neural network model, driven by dual objective physiological metrics, effectively overcomes the limitations of traditional linear prediction and single-source dependency. By prioritizing global fault tolerance over marginal local precision, this approach provides a highly robust, safe, and reliable automated starting map for CI programming, proving especially valuable for uncooperative patients.
OBJECTIVES: To investigate the correlations between NRT thresholds, EABR thresholds, and subjective behavioral threshold T/C-levels in CI recipients, and to develop an artificial neural network-based prediction model for CI fitting parameters.
METHODS: NRT thresholds, EABR thresholds, and subjective behavioral threshold T/C-levels across electrodes 1, 6, 11, 16, and 22 were collected from 37 CI recipients, followed by correlation analyses. Using NRT and EABR thresholds as combined input features, a backpropagation (BP) neural network integrated with a Bayesian regularization (BR) mechanism (BR-BP) was constructed to predict the T- and C-levels.
RESULTS: Both NRT and EABR thresholds exhibited significant positive correlations with T- and C-levels (P < 0.05). A three-dimensional linear prediction model directly combining NRT and EABR thresholds failed due to severe multicollinearity, yielding a maximum mean absolute error (MAE) of up to 373.86 CL. In contrast, the BR-BP neural network successfully achieved nonlinear feature decoupling, effectively mitigating the overfitting commonly associated with small sample sizes, and stably constrained the prediction errors across all tested electrodes to within approximately 60 CL.
CONCLUSIONS: NRT and EABR thresholds exhibit significant correlations with subjective behavioral CI fitting parameters. While single-source predictive models may occasionally offer localized accuracy on certain electrodes, their vulnerability to signal loss limits their clinical safety. The proposed neural network model, driven by dual objective physiological metrics, effectively overcomes the limitations of traditional linear prediction and single-source dependency. By prioritizing global fault tolerance over marginal local precision, this approach provides a highly robust, safe, and reliable automated starting map for CI programming, proving especially valuable for uncooperative patients.