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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Artificial intelligence based software facilitates spirometry quality control in asthma and COPD clinical trials.

Eva Topole1, Sonia Biondaro1, Isabella Montagna1

  • 1Global Clinical Development, Chiesi Farmaceutici, S.p.A., Parma, Italy.

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Artificial intelligence (AI) software accurately assesses spirometry quality in clinical trials, matching expert performance. This AI tool can reduce variability and improve data consistency in respiratory research.

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Area of Science:

  • Pulmonary Medicine
  • Medical Informatics
  • Clinical Trial Methodology

Background:

  • High-quality spirometry data is crucial for clinical trials, especially for endpoints like forced expiratory volume in 1-second (FEV1) and forced vital capacity (FVC).
  • Current American Thoracic Society (ATS)/European Respiratory Society (ERS) standards for spirometry quality include subjective evaluations, leading to inter-rater variability and potential errors.

Purpose of the Study:

  • To evaluate the effectiveness of an artificial intelligence (AI)-based software (ArtiQ.QC) in assessing spirometry quality.
  • To compare the AI's performance against traditional over-reading by experts.

Main Methods:

  • A random sample of 2000 spirometry sessions (8258 curves) from COPD and asthma trials was analyzed.
  • Spirometry acceptability was assessed using 2005 ATS/ERS standards by both expert over-readers and the ArtiQ.QC AI software.
  • A subset of curves underwent joint review by three respiratory physicians to establish a consensus (gold standard).

Main Results:

  • The AI software demonstrated high agreement (91%) with expert over-readers, achieving 97% sensitivity and 93% positive predictive value.
  • While 88% of all curves were of good quality, the AI's performance was notably better in the asthma cohort.
  • In a subset analysis, the AI achieved 73% agreement with the expert consensus, outperforming individual over-reader agreement (46%).

Conclusions:

  • AI-based software offers comparable accuracy to human experts in measuring spirometry data quality.
  • The subjective nature of spirometry assessment introduces variability, even with precise criteria.
  • AI can enhance clinical trial conduct by providing consistent results and immediate feedback, thereby reducing variability.