Early detection of interstitial lung disease using AI-powered spirometry

Abstract

Interstitial lung disease (ILD) patients are often misdiagnosed or diagnosed late. Earlier diagnosis allows earlier access to correct treatment, consequently improved survival and quality of life. We investigated if AI software can detect patterns of ILD on spirometry earlier to formal diagnosis.

All subjects from UK Biobank with ILD as cause of death but without clinical ILD diagnosis at the time of spirometry, were selected (N=109). Spirometry data and subject demographics (gender, age, BMI, race, smoking) were input to an AI clinical decision support software (ArtiQ.PFT), which calculates a probability of having present one of lung diseases.

Time between last recorded spirometry and clinical ILD diagnosis was 3.8±1.9 years, with 1.1±1.2y more to ILD death. AI detected ILD in 27% of subjects (N=29), 3.8±2.1 years prior to ILD diagnosis through standard care. 66% (N=19) of these subjects had normal lung function, 34% (N=10) were restrictive. In remaining 73% (N=80), AI most commonly pointed to no disease (39%) or was uncertain (18%). Spirometry in the group where AI identified ILD were significantly different from those where ILD was not detected [FVC%pred 76±12 vs. 87±17 (p=0.003), FEV1%pred 80±13 vs. 84±21 (p=0.36), PEF%pred 103±25 vs. 87±29 (p<0.0001), FEV1/FVC 0.82±0.05 vs. 0.75±0.09 (p=0.007)]. There was no difference in time to mortality, nor in time between spirometry and clinical diagnosis among groups.

In one-quarter of patients, AI detected ILD many years before clinical diagnosis. Most had lung function within normal ranges, suggesting that AI may detect ILD prior to standard interpretation. With further validation, merging AI-support and regular spirometry testing may lead to earlier ILD diagnosis.