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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Acute illness is severe and...
Force Classification01:22

Force Classification

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Classification of Signals

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Classification of Systems-II01:31

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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

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Related Experiment Video

Updated: Jul 9, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Classifying voice disorders for machine learning: a pilot study using the USVAC-C2025 diagnostic framework.

Catherine Madill1, Zhou Hao Leong1, Dharshini Manoharan1

  • 1Voice Research Laboratory, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.

Frontiers in Digital Health
|July 8, 2026
PubMed
Summary

A new multilayer classification system for voice disorders shows high reliability for machine learning applications. This structured approach improves diagnostic consistency, aiding the development of accurate voice disorder classification tools.

Keywords:
artificial intelligencediagnostic classificationinter-rater reliabilitymachine learningvideostroboscopyvoice disorders

Related Experiment Videos

Last Updated: Jul 9, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Area of Science:

  • Otolaryngology and Speech-Language Pathology
  • Computational Linguistics and Machine Learning
  • Diagnostic Reliability Studies

Background:

  • Machine learning for voice disorders requires accurate diagnostic classification, but progress is hindered by inconsistent labeling and a lack of reproducible frameworks.
  • Existing methods lack a standardized system suitable for both clinical practice and computational analysis, limiting the development of reliable machine learning models.

Purpose of the Study:

  • To develop and evaluate a multilayer classification system for voice disorder diagnosis specifically for machine learning applications.
  • To determine the inter- and intra-rater reliability of this framework among otolaryngologists and speech-language pathologists.

Main Methods:

  • A diagnostic reliability study involving 45 adults with voice disorders assessed at a tertiary voice clinic.
  • Development of a five-level hierarchical classification framework through multidisciplinary consensus.
  • Independent application of the framework by four blinded raters to anonymized clinical and video data, with repeated cases for intra-rater analysis. Reliability quantified using Fleiss kappa and intraclass correlation coefficients.

Main Results:

  • High intra-rater reliability was observed (ICC range: 0.768-0.865) across disciplines.
  • Strong inter-rater reliability was found for identifying disordered vs. non-disordered voices (κ = 0.812) and major etiological categories (κ = 0.695).
  • Agreement decreased with diagnostic specificity, particularly for muscle tension disorders (κ = 0.253) and vocal fold paresis (κ = 0.238), while functional neurological voice disorders and structural lesions showed higher agreement.

Conclusions:

  • A structured, multilayer framework enhances diagnostic consistency, crucial for machine learning systems relying on stable labels.
  • The system identifies key areas of diagnostic ambiguity, providing a practical foundation for creating reliable annotated datasets.
  • This framework supports the future development of machine learning tools for voice disorder classification and clinical decision support.