This paper is in the following e-collection/theme issue:

Digital Health Reviews (1186) Machine Learning (1385) Hearing Impairment and Hearing Loss (64) ENT (Ear, Nose, Throat) (21) Development of Novel Medical Devices and Innovations for Existing Devices (70) Digital Health, Telehealth and e-Innovation in Clinical Settings (344)

Published on in Vol 24, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32581, first published .
Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review

Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review

Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review

Authors of this article:

Jan-Willem Wasmann1 Author Orcid Image ;   Leontien Pragt1 Author Orcid Image ;   Robert Eikelboom2, 3, 4 Author Orcid Image ;   De Wet Swanepoel2, 3, 4 Author Orcid Image
Article Authors Cited by (22) Tweetations (24) Metrics

Jan-Willem Wasmann   1 * , MSc ;   Leontien Pragt   1 * , MSc ;   Robert Eikelboom   2, 3, 4 , PhD ;   De Wet Swanepoel   2, 3, 4 , PhD

1 Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands

2 Ear Science Institute Australia, Subiaco, Australia

3 Ear Sciences Centre, Medical School, The University of Western Australia, Perth, Australia

4 Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa

*these authors contributed equally

Corresponding Author:

  • Jan-Willem Wasmann, MSc
  • Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour
  • Radboud University Medical Centre
  • Philips van Leydenlaan 15
  • Nijmegen, 6500 HB
  • Netherlands
  • Phone: 31 024 361 04 2
  • Email: Jan-Willem.Wasmann@radboudumc.nl