Deep Learning-based Estimated Pulmonary Biological Age from Chest CT Images in Healthy Adults: a model development and validation study.

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Abstract

Estimated pulmonary biological age (ePBA) has emerged as a more reliable indicator for disease progression and mortality than chronological age, with chest CT as a promising tool for calculating ePBA. However, the lack of models trained and validated with large-scale healthy adults hinders the generalizability of the CT-based ePBA. To develop an ageing biomarker (ePBA) from multi-center chest CTs of healthy adults using deep learning and investigate the association between age gap (ePBA－chronological age) and pulmonary function as well as all-cause mortality in patients with chronic obstructive pulmonary disease (COPD). We utilized 11,187 chest CT scans from healthy adults at three health management centers and used multiple deep-learning models. Of these, 7,726 scans from Institution A were used for model development. The remaining CT scans from Institution B (n=1,506) and C (n=1,955) served as external test datasets. To examine whether ePBA provided information beyond chronological age in patients with the disease, we investigated the association of age gap with lung function and all-cause mortality among 138 COPD patients hospitalized at the same time period in institution A. The deep learning models demonstrated acceptable applicability for this task, exhibited a strong correlation between ePBA and chronological age. Age gap was significantly associated with FEV1% reduction (rs=-0.18, P=.03) and an increased risk of all-cause mortality (HR: 1.16 [ 95%CI: 1.08, 1.25]) in patients with COPD. This study developed and validated the biomarker of ageing-ePBA- with deep learning models based on chest CT. Age gap could serve as a novel clinical biomarker in COPD patients.

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