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Updated: Jun 22, 2025

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An Open-Source Deep Learning-Based Toolbox for Automated Auditory Brainstem Response Analyses (ABRA).

Abhijeeth Erra1, Jeffrey Chen1, Cayla M Miller2

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

Biorxiv : the Preprint Server for Biology
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Auditory Brainstem Response Analyzer (ABRA), an AI tool that automates hearing loss analysis using deep learning. ABRA standardizes auditory brainstem response (ABR) measurements, improving accuracy and efficiency 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.
  • Manual ABR analysis is subjective and lacks reproducibility.

Purpose of the Study:

  • To develop an automated tool for analyzing ABR waveforms.
  • To improve the speed, accuracy, and reproducibility of ABR analysis.
  • To facilitate interdisciplinary research in hearing science.

Main Methods:

  • Developed Auditory Brainstem Response Analyzer (ABRA), an open-source GUI tool.
  • Utilized deep learning, specifically convolutional neural networks, for waveform analysis.
  • Trained models on diverse ABR datasets from multiple experimental settings.

Main Results:

  • ABRA achieved rapid and unbiased extraction of ABR metrics (amplitude, latency, thresholds).
  • Deep learning models demonstrated performance comparable to expert human analysis.
  • Analysis time was significantly reduced, and reproducibility was enhanced across datasets.

Conclusions:

  • ABRA offers a standardized, automated solution for ABR analysis.
  • The tool enhances research reproducibility and efficiency in hearing science.
  • ABRA bridges hearing research, neuroscience, and computational techniques for broader insights.