Recent AI breakthroughs can automate quantification of imaging features. Distinguishing Group 1 and 3 PH is challenging in patients with ?mild? lung disease due to overlapping clinical characteristics. This multicentre study develops and deploys a CT AI model to quantify and establish the prognostic value of lung parenchymal changes.

521 consecutive patients between 2001-19 were included from the ASPIRE registry. A novel AI model which automatically classifies the lung parenchyma, and provides the percentage of normal, ground glass (GG), ground glass reticulation (GGR), honeycombing and emphysema was developed. Fibrosis severity was scored by specialist radiologists. Multivariate cox regression adjusted for age, sex, WHO function class, and DLCO. Findings were externally validated in 246 patients (33 centres, 37 scanners).

All patterns were univariate predictors. GGR% (HR 1.02, p=0.015) and fibrosis% (HR 1.01, p=0.05) were continuous multivariate predictors. 2% GGR and 4% fibrosis corresponded to 20% 1-year mortality. In the external cohort, these thresholds were multivariate predictors (2% GGR HR 1.74, p=0.011 and 4% fibrosis HR 1.85, p=0.004). In 300 patients radiologically scored as having ?no? fibrosis, AI identified minor disease (1.2% GGR) which was prognostic (HR 1.03, p=0.006). Adding GGR to a predictive model of radiologically scored disease significantly improved the model (c-index 0.763 vs 0.742, p=0.038).

This is the largest AI study in this domain. GGR% and fibrosis% are indepedent prognostic markers. AI is sensitive to minor disease, and provides additional predictive value used in combination with radiological reporting.