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Related Experiment Video

Updated: Sep 17, 2025

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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An Open-Source Deep Learning-Based GUI Toolbox for Automated Auditory Brainstem Response Analyses (ABRA).

Abhijeeth Erra1, Jeffrey Chen1, Cayla M Miller2

  • 1Data Institute, University of San Francisco, San Francisco, CA.

Research Square
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning tool, Auditory Brainstem Response Analyzer (ABRA), automates hearing loss analysis. This innovation standardizes auditory brainstem response (ABR) measurements, improving accuracy and reproducibility in hearing research.

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Area of Science:

  • Neuroscience
  • Bioengineering
  • Computational Biology

Background:

  • Hearing loss is a global health issue linked to cognitive decline.
  • Auditory brainstem responses (ABRs) are crucial for assessing auditory pathway function.
  • Current ABR analysis is subjective, limiting reproducibility.

Purpose of the Study:

  • To develop an automated tool for analyzing ABR waveforms.
  • To standardize ABR metric extraction for improved research consistency.
  • To enhance the efficiency and reliability of hearing loss diagnostics.

Main Methods:

  • Developed Auditory Brainstem Response Analyzer (ABRA), an open-source, deep learning-powered GUI.
  • Utilized convolutional neural networks trained on diverse ABR datasets.
  • Automated extraction of ABR metrics like peak amplitude, latency, and auditory thresholds.

Main Results:

  • ABRA achieved performance comparable to expert human analysis.
  • The tool significantly reduced ABR analysis time.
  • Demonstrated enhanced reproducibility across multi-laboratory datasets.

Conclusions:

  • ABRA offers automated, standardized ABR analysis, overcoming traditional limitations.
  • The tool facilitates interdisciplinary insights in hearing research and neuroscience.
  • ABRA is accessible online, promoting wider adoption and collaboration.