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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Artificial Intelligence for Spirometry Quality Evaluation: A Systematic Review.

Julia López-Canay1, Manuel Casal-Guisande1,2,3, Cristina Represas-Represas1,3,4

  • 1NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain.

Bioengineering (Basel, Switzerland)
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for improving spirometry quality control in primary care. AI tools can help assess test acceptability and detect errors, but more research is needed for clinical implementation.

Keywords:
acceptabilityartificial intelligencedeep learningerror detectionintelligent systemmachine learningquality controlspirometry

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Area of Science:

  • Pulmonary Medicine
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Spirometry is crucial for diagnosing respiratory diseases but faces quality control challenges in non-specialized settings.
  • Artificial intelligence (AI) offers potential solutions for enhancing spirometry reliability.

Purpose of the Study:

  • To systematically review the evidence on AI applications for spirometry quality control.
  • To assess the effectiveness of AI in ensuring spirometry result reliability.

Main Methods:

  • Systematic literature search in PubMed and IEEE Xplore (2014-June 2025).
  • PRISMA guidelines followed for search and data extraction.
  • Analysis of six included peer-reviewed studies.

Main Results:

  • Four studies focused on maneuver acceptability/usability; two on error detection.
  • Convolutional neural networks were the most common AI models used.
  • Three AI models achieved an area under the ROC curve > 0.88.

Conclusions:

  • AI tools demonstrate significant potential for spirometry quality control, aiding acceptability assessment and error detection.
  • Current research is limited and heterogeneous, necessitating broader, multicenter validation.
  • Further research is required to confirm clinical utility and facilitate widespread implementation.