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Classifying voice disorders is challenging due to varied terminology and systems. This review highlights limited agreement in theoretical classifications, though automated methods show promise for consistent voice disorder diagnosis.

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Area of Science:

  • Laryngology
  • Speech-Language Pathology
  • Computational Linguistics

Background:

  • Voice classification systems lack standardization, hindering global professional communication and clinical management.
  • Divergent terminologies and historical classification difficulties impede scientific discourse on voice disorders.

Purpose of the Study:

  • To map and analyze diverse diagnostic classifications for voice disorders.
  • To describe findings from a scoping review of theoretical and automated classification systems.

Main Methods:

  • Conducted a scoping review using electronic and manual searches.
  • Classified 44 included articles into theoretical propositions (G1) and automated computerized systems (G2).
  • Analyzed classification structures, etiology focus (G1), and acoustic/machine learning approaches (G2).

Main Results:

  • Group G1 (theoretical) featured classifications from 2-11 main groups, primarily focusing on etiology, with studies from 1947-2021.
  • Group G2 (automated) presented classifications from 4-7 groups, emphasizing laryngeal conditions, with studies from 2009 onwards.
  • Limited convergence observed between theoretical classification systems, necessitating subgroups for diverse vocal disorders.

Conclusions:

  • Theoretical voice disorder classifications show significant variability.
  • Automated classification systems offer potential for reduced variability but require theoretical grounding for clinical application.
  • Further research is needed to bridge theoretical frameworks and computational approaches for unified voice disorder classification.