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Voice disorder recognition using machine learning: a scoping review protocol.

Rijul Gupta1, Dhanshree R Gunjawate2, Duy Duong Nguyen2

  • 1School of Electrical and Information Engineering, The University of Sydney Faculty of Engineering and Information Technologies, Sydney, New South Wales, Australia.

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|February 24, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) shows promise for detecting voice disorders, but reliability issues prevent clinical use. This review identifies factors hindering ML algorithm adoption in healthcare settings.

Keywords:
Factor Analysis, StatisticalInformation technologyOTOLARYNGOLOGYSTATISTICS & RESEARCH METHODSSpeech pathologyTelemedicine

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Area of Science:

  • Speech and Hearing Sciences
  • Biomedical Engineering
  • Computer Science

Background:

  • Machine learning (ML) algorithms demonstrate high accuracy in detecting voice disorders.
  • ML holds potential for aiding clinicians in voice disorder analysis and treatment evaluation.
  • Despite research, no ML algorithms are currently reliable enough for widespread clinical application.

Purpose of the Study:

  • To identify critical issues impeding the clinical use of ML algorithms for voice disorder detection.
  • To pinpoint standard audio tasks, acoustic features, processing algorithms, and environmental factors influencing ML efficacy.

Main Methods:

  • A comprehensive literature search was conducted across seven major databases (Web of Science, Scopus, Compendex, CINAHL, Medline, IEEE Explore, Embase) from 2013 to 2023.
  • Search strategy was refined with university library assistance for database-specific syntax.
  • Data selection, extraction, and synthesis followed the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews' (PRISMA-ScR) guidelines.

Main Results:

  • This section is to be populated upon completion of the review.

Conclusions:

  • This scoping review will provide insights into the challenges and limitations of applying ML in clinical voice disorder detection.
  • Findings will guide future research to develop more robust and clinically viable ML solutions for voice pathology.