Deep Learning of Suboptimal Spirometry to Predict Respiratory Outcomes and Mortality.

ABSTRACT:

Importance

Obtaining spirometry requires repeated testing and using the maximal values based on quality control criteria. Whether the suboptimal efforts are useful for the prediction of respiratory outcomes is not clear.

Objective

To determine whether a machine learning model could predict respiratory outcomes and mortality based on suboptimal spirometry.

Design

Observational cohorts (UK Biobank and COPDGene).

Setting

Multi-center; population, and disease-enriched.

Participants

UK aged 40-69; US aged 45-80, >10 pack-years smoking, without respiratory diseases other than COPD or asthma.

Exposures

Raw spirograms (volume-time).

Main outcomes and measures

To create a combined representation of lung function we implemented a contrastive learning

SUBMITTER: Hill D 

PROVIDER: S-EPMC12236919 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature