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.

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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
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article

Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study

Deog Kyeom Kim, Heemoon Park, Jung‐Kyu Lee, Eun Young Heo et al.
8 citations
Journal of Medical Internet Research
Chronic Obstructive Pulmonary Disease (COPD) Research
12.15
article

Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study

Deog Kyeom Kim, Heemoon Park, Jung‐Kyu Lee, Eun Young Heo, Eun‐Tae Jeon, Dong Hyun Kim, Hyun Woo Lee, Chang‐Hoon Lee
article en
8 citations

Abstract

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.

Journal of Medical Internet ResearchVol. 27
Seoul National University (KR), Korea University (KR), Seoul Metropolitan Government (KR), Seoul National University Hospital (KR), SNUH SMG-SNU Boramae Medical Center (KR)
Good health and well-being
Openalex Percentile: Top 1%
Chronic Obstructive Pulmonary Disease (COPD) Research
12.15
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Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study — Deog Kyeom Kim, Heemoon Park, et al. · Journal of Medical Internet Research (2025) | TGRS Research Map | TGRS