Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study
The AI-PFT-Clin model, by integrating clinical data with spirometry images, offers enhanced predictive accuracy for AE-COPD events compared to a clinical-only approach. This AI-based framework facilitates the early identification of high-risk individuals through the detection of physiological abnormalities not captured by conventional metrics. The model's robust performance and long-term predictive stability suggest its potential utility in proactive COPD management and personalized intervention planning. These findings highlight the promise of incorporating advanced AI techniques into routine COPD management, particularly in populations traditionally seen as lower risk, supporting improved management of COPD through tailored patient care.
Authors
- Deog Kyeom Kim (ORCID: https://orcid.org/0000-0001-9379-8098)
- Heemoon Park (ORCID: https://orcid.org/0000-0002-0240-2113)
- Jung‐Kyu Lee (ORCID: https://orcid.org/0000-0001-5060-7255)
- Eun Young Heo (ORCID: https://orcid.org/0000-0003-3803-4903)
- Eun‐Tae Jeon (ORCID: https://orcid.org/0000-0002-7000-9719)
- Dong Hyun Kim (ORCID: https://orcid.org/0000-0002-3871-7002)
- Hyun Woo Lee (ORCID: https://orcid.org/0000-0003-4379-0260)
- Chang‐Hoon Lee (ORCID: https://orcid.org/0000-0001-9960-1524)
Institutions
- Seoul National University (KR)
- Korea University (KR)
- Seoul Metropolitan Government (KR)
- Seoul National University Hospital (KR)
- SNUH SMG-SNU Boramae Medical Center (KR)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-15
- DOI
- https://doi.org/10.2196/69785
- Citations
- 8
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 12.15