Feasibility of opportunistic screening for preserved ratio impaired spirometry using chest radiography-based deep learning models.

Background: Although deep learning models using chest radiographs can estimate spirometry measurements, further investigation is needed to evaluate their ability to screen for preserved ratio impaired spirometry (PRISm), an important pre-COPD subtype. This study assessed whether a chest radiograph-based deep learning model can accurately detect PRISm and perform efficient opportunistic screening. Methods: This retrospective study included 54654 paired chest radiography and spirometry datasets from 38470 health checkup participants at a Japanese institution who underwent chest radiography and spirometry on the same day in 2018 and 2019. The dataset of 80% participants was used for model development, and the remaining 20% was used for performance evaluation. We developed a deep learning model that used frontal chest radiographs and demographic scalar inputs to detect PRISm and other pulmonary dysfunction subtypes, including FEV₁ decline, FVC decline, FVC and FEV₁ decline, and airflow limitation. Model performance was evaluated on the internal testing dataset. Subgroup analyses were performed across five independent factors: age, height, ppFVC, ppFEV₁, and FEV₁/FVC. Results: The model achieved an AUROC of 0.892 (95% CI, 0.875-0.906), a sensitivity of 71.6%, and a specificity of 87.9% for PRISm detection on the testing dataset (N = 10917). No significant differences in AUROC were observed across subgroups defined by age, height, FEV₁/FVC, or ppFEV₁. The AUROC values for detecting all pulmonary dysfunction subtypes exceeded 0.8. Conclusions: The chest radiography-based model can effectively detect PRISm and may be useful for opportunistic screening of PRISm for supporting early risk management and intervention for pre-COPD conditions and obstructive pulmonary diseases.