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Deep Learning-Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time
Eun-Tae Jeon1, Heemoon Park2, Jung-Kyu Lee2
1Department of Neurology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea.
Journal of Medical Internet Research
|May 15, 2025
Summary
An AI model integrating clinical data and spirometry images accurately predicts acute exacerbations of COPD (AE-COPD). This approach enhances early identification of high-risk individuals for proactive management.
Area of Science:
- Pulmonary Medicine
- Artificial Intelligence in Healthcare
- Predictive Analytics
Background:
- Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition with significant morbidity from acute exacerbations (AE-COPD).
- Traditional spirometry metrics capture limited physiological data, underestimating AE-COPD risk.
- Artificial intelligence (AI) offers advanced analysis of full spirometry curves to identify complex patterns indicative of exacerbation risk.
Purpose of the Study:
- To evaluate if an AI-enhanced predictive model integrating clinical data and spirometry images improves AE-COPD prediction accuracy.
- To compare the AI-PFT-Clin model against a clinical-only model for predicting moderate-to-severe and severe AE-COPD events.
Main Methods:
- A retrospective cohort study of 10,492 COPD cases (2004-2020) using registry data.
- Developed an AI-PFT-Clin model combining clinical variables and spirometry images (flow-volume, volume-time curves).
- Compared AI-PFT-Clin model performance against a Clin model (clinical variables only) for predicting AE-COPD events within one year.
Main Results:
- The AI-PFT-Clin model demonstrated superior predictive accuracy in an external validation cohort (AUC 0.755 vs. 0.730 for moderate-to-severe AE-COPD; 0.713 vs. 0.675 for severe AE-COPD).
- Higher AI-PFT-Clin scores significantly correlated with increased AE-COPD risk (aHR 4.21).
- The model showed sustained predictive stability over a 10-year follow-up and reliable performance across subgroups.
Conclusions:
- Integrating clinical data with spirometry images using AI significantly enhances AE-COPD prediction accuracy over clinical data alone.
- The AI-PFT-Clin model facilitates early identification of high-risk individuals by detecting subtle physiological abnormalities.
- This AI-based approach holds potential for proactive COPD management and personalized intervention strategies.
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