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

2024-05-06

AI Summary

To address the challenge of early COPD risk identification, this paper proposes DeepSpiro—a novel end-to-end deep learning framework that performs both disease detection and 1–5-year risk prediction directly from volumetric flow-time spirometry sequences. Methodologically, DeepSpiro introduces a four-module synergistic architecture: (i) SpiroSmoother enhances signal stability; (ii) SpiroEncoder captures multi-scale volumetric variability; (iii) SpiroExplainer integrates heterogeneous clinical and imaging data with a volume-aware attention mechanism to improve interpretability; and (iv) SpiroPredictor focuses on concave morphological features in critical waveform segments for long-term progression forecasting. Evaluated on the UK Biobank cohort, DeepSpiro achieves an AUC of 0.8328 for COPD detection and statistically significant risk prediction (p < 0.001), outperforming all baselines. This work is the first to unify time-series pulmonary function modeling, interpretable attention, and multimodal longitudinal risk prediction within a clinically deployable framework, establishing a new paradigm for early screening and timely intervention in chronic lung diseases.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Chronic Obstructive Pulmonary Disease
• Early Prediction
• Risk Assessment

Innovation

• DeepSpiro
• COPD Prediction
• Pulmonary Function Testing