Computer Vision-based Extraction of Structured Data From Scanned Audiograms in the Electronic Health Record
CV can be used to accurately extract pure-tone threshold data from scanned audiogram test sheets without reliance on deep learning or manual preprocessing. The method offers a scalable solution for transforming legacy audiograms into structured data sets suitable for population-level hearing research, clinical decision support, and epidemiologic surveillance.
Authors
- Peter R. Dixon (ORCID: https://orcid.org/0000-0002-3537-5078)
- Carl Ehrett (ORCID: https://orcid.org/0000-0002-0711-0347)
- Ruoyu Yang (ORCID: https://orcid.org/0000-0002-9147-5030)
- Dana Mae Salvador
Institutions
- Medical University of South Carolina (US)
- Clemson University (US)
Publication Details
- Journal
- Otology & Neurotology
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1097/mao.0000000000004679
- Primary Topic
- Hearing Loss and Rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 0.00