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Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods,

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Machine learning (ML) advances remote respiratory screening using sound analysis. This review covers ML methods, audio databases, and highlights areas needing further research for AI in respiratory auscultation.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Pulmonology

Background:

  • Auscultation is vital for clinical diagnosis.
  • Machine learning (ML) is being explored for automated respiratory sound analysis.
  • Remote screening for respiratory conditions is a growing research area.

Purpose of the Study:

  • To provide a comprehensive overview of ML applications in respiratory auscultation.
  • To highlight available audio datasets for ML model development.
  • To identify under-addressed challenges in the field.

Main Methods:

  • Narrative review of recent scientific literature.
  • Analysis of publicly available audio databases for respiratory sounds.
  • Summarization of machine learning techniques applied to auscultation data.

Main Results:

  • Identification of key ML methods used in respiratory sound analysis.
  • Cataloging of relevant audio datasets for research.
  • Discussion of recent trends, including COVID-19 detection using audio data.

Conclusions:

  • The field of AI-driven respiratory auscultation is rapidly evolving.
  • Standardized datasets and further research into ML methods are needed.
  • This review facilitates AI adoption in respiratory diagnostics.