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arXiv · 2405.03239

Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series

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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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, 2, 3, 4, 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 predicts the long-term progression of the COPD disease.

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Shuhao Mei, Xin Li, Yuxi Zhou, Jiahao Xu, Yong Zhang, Yuxuan Wan, Shan Cao, Qinghao Zhao, Shijia Geng, Junqing Xie, Shengyong Chen, Shenda Hong. 2024-12-28. Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series. https://doi.org/10.1038/s41540-025-00489-y

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