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Weininger O, Warnecke A, Lesinski-Schiedat A, et al. Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology. Audiol Res. 2019;9(2):230doi: 10.4081/audiores.2019.230.
Weininger, O., Warnecke, A., Lesinski-Schiedat, A., Lenarz, T., & Stolle, S. (2019). Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology. Audiology research, 9(2), 230. https://doi.org/10.4081/audiores.2019.230
Weininger, Oren, et al. "Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology." Audiology research vol. 9,2 (2019): 230. doi: https://doi.org/10.4081/audiores.2019.230
Weininger O, Warnecke A, Lesinski-Schiedat A, Lenarz T, Stolle S. Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology. Audiol Res. 2019 Nov 05;9(2):230. doi: 10.4081/audiores.2019.230. eCollection 2019 Sep 02. PMID: 31728177; PMCID: PMC6843421.
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Audiol Res. 2019 Nov 05;9(2):230. doi: 10.4081/audiores.2019.230. eCollection 2019 Sep 02.

Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology.

Audiology research

Oren Weininger, Athanasia Warnecke, Anke Lesinski-Schiedat, Thomas Lenarz, Stefan Stolle

Affiliations

  1. Department of Otolaryngology, Hannover Medical School, Hannover.
  2. Cluster of Excellence, Hearing4all German Research Foundation, Hannover, Germany.

PMID: 31728177 PMCID: PMC6843421 DOI: 10.4081/audiores.2019.230

Abstract

Genetic contribution to progressive hearing loss in adults is underestimated. Established machine learning-based software could offer a rapid supportive tool to stratify patients with progressive hearing loss. A retrospective longitudinal analysis of 141 adult patients presenting with hearing loss was performed. Hearing threshold was measured at least twice 18 months or more apart. Based on the baseline audiogram, hearing thresholds and age were uploaded to AudioGene v4® (Center for Bioinformatics and Computational Biology at The University of Iowa City, IA, USA) to predict the underlying genetic cause of hearing loss and the likely progression of hearing loss. The progression of hearing loss was validated by comparison with the most recent audiogram data of the patients. The most frequently predicted

©Copyright: the Author(s), 2019.

Keywords: Audiogram; Genotype; Machine learning; Phenotype; Progressive hearing loss

Conflict of interest statement

Conflict of interest: the authors declare no potential conflict of interest.

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