ArticleFrontiers in medicine2024
Spirometry test values can be estimated from a single chest radiograph.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Feasibility of opportunistic screening for preserved ratio impaired spirometry using chest radiography-based deep learning models.European journal of radiology open · 2026Article
- Article
- Interpretable chronic obstructive pulmonary disease identification using chest X-ray radiomics: a multicenter study.Insights into imaging · 2026Article
- Deep learning based CT images for lung function prediction in patients with chronic obstructive pulmonary disease.BMC pulmonary medicine · 2025Article
- Utility of osteoporosis screening based on estimation of bone mineral density using bidirectional chest radiographs with deep learning models.Frontiers in medicine · 2025Article
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Authors and funding
7 authors.
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Abstract
Introduction: Physical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment. Chest radiographs (CXRs), though acquired frequently, are not used to measure pulmonary functional information. This study aimed to evaluate whether spirometry parameters can be estimated accurately from single frontal CXR without image findings using deep learning. Methods: Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV Results: The MAPEs between the spirometry measurements and AI estimates for FVC, FEV Discussion: Frontal CXRs contain information related to pulmonary function, and AI estimation performed using frontal CXRs without image findings could accurately estimate spirometry values. The network proposed for estimating pulmonary function in this study could serve as a recommendation for performing spirometry or as an alternative method, suggesting its utility.
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