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A Deep Learning Model for Automatic Thresholding of Auditory Brainstem Responses: Multicenter and Multispecies

Yin Liu1,2, Weicheng Xu1, Tiecheng Song1

  • 1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.

Ear and Hearing
|April 9, 2026
PubMed
Summary

This study introduces a novel deep learning model for automatic auditory brainstem response (ABR) threshold estimation. The model demonstrates high accuracy and generalizability across human and mouse datasets, offering potential for clinical applications.

Keywords:
Auditory brainstem responseAutomatic thresholdingDeep learningMulticenter validation

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Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Machine Learning

Background:

  • Auditory Brainstem Response (ABR) testing is crucial for assessing auditory function.
  • Traditional ABR threshold determination relies on manual waveform analysis, which can be time-consuming and subjective.
  • Developing automated methods for ABR threshold estimation is essential for improving efficiency and consistency in clinical practice.

Purpose of the Study:

  • To develop and validate a deep learning model for direct estimation of ABR thresholds from multi-level waveform series.
  • To assess the model's performance on large-scale, diverse human and mouse datasets from multiple centers.
  • To compare the model's accuracy against existing algorithms and evaluate the contribution of its architectural components.

Main Methods:

  • A transformer-based deep learning model was developed, incorporating adjacent-level cross-attention and a supervised contrastive learning branch.
  • The model was trained and validated on extensive human and mouse ABR datasets.
  • Performance was evaluated using exact-match and tolerance accuracies (±5 and ±10 dB), with ablation studies conducted to confirm component contributions.

Main Results:

  • The model achieved high exact-match accuracy (e.g., 93.07% on Human Dataset I) and excellent performance within ±10 dB tolerance (e.g., 99.32% on Human Dataset I).
  • Strong generalization was observed across external datasets, age groups, and hearing conditions.
  • The model surpassed previous algorithms on benchmark datasets and ablation studies confirmed the efficacy of its components.

Conclusions:

  • This study presents the first deep learning model for automated ABR thresholding from multi-level waveform stacks.
  • The model demonstrates robust generalizability across species, centers, and stimulus types, indicating significant clinical potential.
  • Future directions include broader external validation and integration into real-time ABR acquisition systems for enhanced clinical utility.