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.

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.