Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases

Abstract

Background

Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patterns associated with idiopathic pulmonary fibrosis (IPF). We developed and tested new Fibresolve software to predict outcomes in patients with ILD.

Methods

Fibresolve uses a transformer (ViT) algorithm to analyze CT imaging that additionally embeds PFTs, age, and sex to produce an overall risk score. The model was trained to optimize risk score in a dataset of 602 subjects designed to maximize predictive performance via Cox proportional hazards. Validation was completed with the first hazard ratio assessment dataset, then tested in a second datatest set.

Results

61 % of 220 subjects died in the validation set's study period, whereas 40 % of the 407 subjects died in the second dataset's. The validation dataset's mortality hazard ratio (HR) was 3.66 (95 % CI: 2.09–6.42) and 4.66 (CI: 2.47–8.77) for the moderate and high-risk groups. In the second dataset, Fibresolve was a predictor of mortality at initial visit, with a HR of 2.79 (1.73–4.49) and 5.82 (3.53–9.60) in the moderate and high-risk groups. Similar predictive performance was seen at follow-up visits, as well as with changes in the Fibresolve scores over sequential visits.

Conclusion

Fibresolve predicts mortality by automatically assessing combined CT, PFTs, age, and sex into a ViT model. The new software algorithm affords accurate prognostication and demonstrates the ability to detect clinical changes over time.