Audioprofile Surfaces

Author:

Taylor Kyle R.12,Booth Kevin T.3,Azaiez Hela3,Sloan Christina M.4,Kolbe Diana L.3,Glanz Emily N.12,Shearer A. Eliot3,DeLuca Adam P.5,Anand V. Nikhil2,Hildebrand Michael S.3,Simpson Allen C.3,Eppsteiner Robert W.3,Scheetz Todd E.125,Braun Terry A.12,Huygen Patrick L. M.6,Smith Richard J. H.34,Casavant Thomas L.125

Affiliation:

1. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA

2. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa, USA

3. Department of Otolaryngology, Head and Neck Surgery, University of Iowa, Iowa City, Iowa, USA

4. Department of Molecular Physiology and Biophysics, University of Iowa Carver, Iowa City, Iowa, USA

5. Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, USA

6. Department of Otorhinolaryngology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

Abstract

Objective: To present audiometric data in 3 dimensions by considering age as an addition dimension. Methods: Audioprofile surfaces (APSs) were fitted to a set of audiograms by plotting each measurement of an audiogram as an independent point in 3 dimensions with the x, y, and z axes representing frequency, hearing loss in dB, and age, respectively. Results: Using the Java-based APS viewer as a standalone application, APSs were pre-computed for 34 loci. By selecting APSs for the appropriate genetic locus, a clinician can compare this APS-generated average surface to a specific patient’s audiogram. Conclusion: Audioprofile surfaces provide an easily interpreted visual representation of a person’s hearing acuity relative to others with the same genetic cause of hearing loss. Audioprofile surfaces will support the generation and testing of sophisticated hypotheses to further refine our understanding of the biology of hearing.

Publisher

SAGE Publications

Subject

General Medicine,Otorhinolaryngology

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