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Audiology Research is published by MDPI from Volume 10 Issue 2 (2020). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with PAGEPress.
Article

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

1
Department of Otolaryngology, Hannover Medical School, Hannover, Germany
2
Cluster of Excellence, Hearing4all German Research Foundation, Hannover, Germany
*
Author to whom correspondence should be addressed.
Audiol. Res. 2019, 9(2), 27-32; https://doi.org/10.4081/audiores.2019.230
Received: 13 July 2019 / Revised: 10 September 2019 / Accepted: 22 September 2019 / Published: 5 November 2019
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 loci were DFNA2B, DFNA9 and DFNA2A. The frequency of loci/genes predicted by AudioGene remains consistent when using the initial or the final audiogram of the patients. In conclusion, machine learning-based software analysis of clinical data might be a useful tool to identify patients at risk for having autosomal dominant hearing loss. With this approach, patients with suspected progressive hearing loss could be subjected to close audiological followup, genetic testing and improved patient counselling.
Keywords: Machine learning; Progressive hearing loss; Audiogram; Phenotype; Genotype Machine learning; Progressive hearing loss; Audiogram; Phenotype; Genotype
MDPI and ACS Style

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, 9, 27-32. https://doi.org/10.4081/audiores.2019.230

AMA Style

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. Audiology Research. 2019; 9(2):27-32. https://doi.org/10.4081/audiores.2019.230

Chicago/Turabian Style

Weininger, Oren, Athanasia Warnecke, Anke Lesinski-Schiedat, Thomas Lenarz, and Stefan Stolle. 2019. "Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology" Audiology Research 9, no. 2: 27-32. https://doi.org/10.4081/audiores.2019.230

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