Artificial intelligence based software facilitates spirometry quality control in asthma and COPD clinical trials.

ABSTRACT:

Rationale

Acquiring high-quality spirometry data in clinical trials is important, particularly when using forced expiratory volume in 1 s or forced vital capacity as primary end-points. In addition to quantitative criteria, the American Thoracic Society (ATS)/European Respiratory Society (ERS) standards include subjective evaluation which introduces inter-rater variability and potential mistakes. We explored the value of artificial intelligence (AI)-based software (ArtiQ.QC) to assess spirometry quality and compared it to traditional over-reading control.

Methods

A random sample of 2000 sessions (8258 curves) was selected from Chiesi COPD and asthma trials (n=1000 per disease). Acceptability using the 2005 ATS/ERS standards was determined by over-reader review and by ArtiQ.Q

SUBMITTER: Topole E 

PROVIDER: S-EPMC9907146 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature