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April 11, 2026Ear and Hearing0 citations

Deep Learning Model for Automatic Thresholding of Auditory Brainstem Responses

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

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Authors

YLYin LiuWXWeicheng XuTSTiecheng Song

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Overview

Demonstrates a deep learning model for efficient automatic thresholding of auditory brainstem responses, suggesting improved clinical applications.

Key Points

  • The study aims to develop a deep learning model for automatic thresholding of auditory brainstem responses (ABRs) using waveform data.
  • Developed a deep learning model for ABR thresholding
  • Validated the model across multiple centers and species
  • Used multi-level waveform stacks for data analysis
  • Model shows strong generalizability across different stimulus types
  • Demonstrates potential for efficient and automated threshold estimation in clinical settings
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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/69d9e5ec78050d08c1b7617fhttps://doi.org/10.1097/aud.0000000000001811
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning‐Based Framework2024 · 4 citations
  2. 2A Dynamic Time Warping-Aware Series-Temporal Transformer for Automated Thresholding of Auditory Brainstem Responses.2026
  3. 3An open-source deep learning-based toolbox for automated auditory brainstem response analyses (ABRA)2026
  4. 4Toward a Real-time Clinical Decision Support System for Auditory Brainstem Response Threshold Estimation2026
  5. 5Multispecies initial numerical validation of an efficient algorithm prototype for auditory brainstem response hearing threshold estimation2024 · 1 citations