Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1-5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease.

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Publication Details

Journal
npj Systems Biology and Applications
Published
2025-02-15
DOI
https://doi.org/10.1038/s41540-025-00489-y
Citations
16
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
Field-Weighted Citation Impact
24.29

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article

Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series

Junqing Xie, Yuxuan Wan, Shuhao Mei, Yong Zhang et al.
16 citations
npj Systems Biology and Applications
Chronic Obstructive Pulmonary Disease (COPD) Research
24.29
article

Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series

Junqing Xie, Yuxuan Wan, Shuhao Mei, Yong Zhang, Shan Cao, Shengyong Chen, Shijia Geng, Shenda Hong, Xin Li, Yuxi Zhou, Qinghao Zhao, Jiahao Xu
article en
16 citations

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1-5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease.

npj Systems Biology and ApplicationsVol. 11(1)
Tianjin University of Science and Technology (CN), Tianjin University of Technology (CN), Tianjin University (CN), Peking University (CN), University of Oxford (GB), Beijing Academy of Artificial Intelligence (CN), Peking University People's Hospital (CN), Second Hospital of Tianjin Medical University (CN), Oxford BioMedica (United Kingdom) (GB), Beijing Tsinghua Chang Gung Hospital (CN), Tianjin Medical University (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Tianjin City, Peking University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 0%
Chronic Obstructive Pulmonary Disease (COPD) Research
24.29
DOIOpen Access PDFOpenAlex
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