| Title: | Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series |
| Journal: | npj Systems Biology and Applications |
| Published: | 15 Feb 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39955293/ |
| DOI: | https://doi.org/10.1038/s41540-025-00489-y |
Publication 14395
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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.</p>
8 Keywords
- Deep Learning
- Disease Progression
- Early Diagnosis
- Female
- Humans
- Male
- Pulmonary Disease, Chronic Obstructive
- Spirometry
12 Authors
- Shuhao Mei
- Xin Li
- Yuxi Zhou
- Jiahao Xu
- Yong Zhang
- Yuxuan Wan
- Shan Cao
- Qinghao Zhao
- Shijia Geng
- Junqing Xie
- Shengyong Chen
- Shenda Hong
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