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Audiogram Digitization Tool for Audiological Reports

arXiv 2208.14621 ↗First posted 31 Aug 2022cs.CV cs.SDcited by 0

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Abstract (arXiv)

A number of private and public insurers compensate workers whose hearing loss can be directly attributed to excessive exposure to noise in the workplace. The claim assessment process is typically lengthy and requires significant effort from human adjudicators who must interpret hand-recorded audiograms, often sent via fax or equivalent. In this work, we present a solution developed in partnership with the Workplace Safety Insurance Board of Ontario to streamline the adjudication process. In particular, we present the first audiogram digitization algorithm capable of automatically extracting the hearing thresholds from a scanned or faxed audiology report as a proof-of-concept. The algorithm extracts most thresholds within 5 dB accuracy, allowing to substantially lessen the time required to convert an audiogram into digital format in a semi-supervised fashion, and is a first step towards the automation of the adjudication process. The source code for the digitization algorithm and a desktop-based implementation of our NIHL annotation portal is publicly available on GitHub (https://github.com/GreenCUBIC/AudiogramDigitization).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 31 Aug 2022.

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References · 3

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  3. 2021Interpreting Audiograms with Multi-stage Neural Networkscited by 1

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