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

An Open-Source Deep Learning-Based GUI Toolbox for Automated Auditory Brainstem Response Analyses (ABRA) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An Open-Source Deep Learning-Based GUI Toolbox for Automated Auditory Brainstem Response Analyses (ABRA) Abhijeeth Erra, Jeffrey Chen, Cayla M. Miller, Elena Chrysostomou, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6735294/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Hearing loss is a pervasive global health challenge with profound impacts on communication, cognitive function, and quality of life. Recent studies have established age-related hearing loss as a significant risk factor for dementia, highlighting the importance of hearing loss research. Auditory brainstem responses (ABRs), which are electrophysiological recordings of synchronized neural activity from the auditory nerve and brainstem, serve as in vivo readouts for sensory hair cell, synaptic integrity, hearing sensitivity, and other key features of auditory pathway functionality, making them highly valuable for both basic neuroscience research and clinical diagnostics. Despite their utility, traditional ABR analyses rely heavily on subjective manual interpretation, leading to considerable variability and limiting reproducibility across studies. Here, we introduce Auditory Brainstem Response Analyzer (ABRA), a novel open-source graphical user interface powered by deep learning, which automates and standardizes ABR waveform analysis. ABRA employs convolutional neural networks trained on diverse datasets collected from multiple experimental settings, achieving rapid and unbiased extraction of key ABR metrics, including peak amplitude, latency, and auditory threshold estimates. We demonstrate that ABRA’s deep learning models provide performance comparable to expert human annotators while dramatically reducing analysis time and enhancing reproducibility across datasets from different laboratories. By bridging hearing research, sensory neuroscience, and advanced computational techniques, ABRA facilitates broader interdisciplinary insights into auditory function. An online version of the tool is available for use at no cost at https://abra.ucsd.edu . Biological sciences/Neuroscience/Auditory system Biological sciences/Biological techniques/Electrophysiology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 02 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6735294","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":473099508,"identity":"672cd857-d8fe-4ab2-bc21-bc029600c009","order_by":0,"name":"Abhijeeth Erra","email":"","orcid":"","institution":"University of San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Abhijeeth","middleName":"","lastName":"Erra","suffix":""},{"id":473099509,"identity":"badf8d04-2bbb-4d84-8519-564ed82910c6","order_by":1,"name":"Jeffrey Chen","email":"","orcid":"","institution":"University of San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Chen","suffix":""},{"id":473099510,"identity":"1fe07da4-b1f1-4519-b455-40f148ce3994","order_by":2,"name":"Cayla M. 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Recent studies have established age-related hearing loss as a significant risk factor for dementia, highlighting the importance of hearing loss research. Auditory brainstem responses (ABRs), which are electrophysiological recordings of synchronized neural activity from the auditory nerve and brainstem, serve as in vivo readouts for sensory hair cell, synaptic integrity, hearing sensitivity, and other key features of auditory pathway functionality, making them highly valuable for both basic neuroscience research and clinical diagnostics. Despite their utility, traditional ABR analyses rely heavily on subjective manual interpretation, leading to considerable variability and limiting reproducibility across studies. Here, we introduce Auditory Brainstem Response Analyzer (ABRA), a novel open-source graphical user interface powered by deep learning, which automates and standardizes ABR waveform analysis. ABRA employs convolutional neural networks trained on diverse datasets collected from multiple experimental settings, achieving rapid and unbiased extraction of key ABR metrics, including peak amplitude, latency, and auditory threshold estimates. We demonstrate that ABRA\u0026rsquo;s deep learning models provide performance comparable to expert human annotators while dramatically reducing analysis time and enhancing reproducibility across datasets from different laboratories. By bridging hearing research, sensory neuroscience, and advanced computational techniques, ABRA facilitates broader interdisciplinary insights into auditory function. 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