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ORIGINAL RESEARCH article

Front. Physiol.

Sec. Respiratory Physiology and Pathophysiology

This article is part of the Research TopicKey Topics in Integrative Physiology - Consolidating Whole-Body Physiology Using Cutting-Edge KnowledgeView all 4 articles

Evaluation of the Impact of Cardiopulmonary Rehabilitation Exercise Training on Cardiopulmonary Function in Patients with Chronic Obstructive Pulmonary Disease Complicated by Unstable Angina Pectoris Using a Hierarchical Deep Learning CT Image Model

Provisionally accepted
Kongyu  XingKongyu Xing1Chunmiao  TanChunmiao Tan1Xiaoling  ChengXiaoling Cheng2*Fen  JiangFen Jiang3
  • 1The First Affiliated Hospital of Hainan Medical University, Haikou, China
  • 2Quanzhou Third Hospital,Quanzhou,China., Quanzhou, China
  • 3International School of Nursing, Hainan Medical University, Haikou, China

The final, formatted version of the article will be published soon.

Objective: This study aimed to quantitatively evaluate the impact of cardiopulmonary rehabilitation (CPR) exercise training on cardiopulmonary structure and function in patients with chronic obstructive pulmonary disease (COPD) complicated by unstable angina (UA) pectoris, based on a hierarchical deep learning (DL) CT image model. Methods: This prospective randomized controlled trial enrolled 400 patients with COPD complicated by UA pectoris, stratified according to GOLD grades (I-IV), who were randomly allocated to an experimental group (EG, n=200, receiving 12 weeks of standard CPR training) and a control group (CG, n=200, receiving conventional care). A multi-task 3D U-Net + ResNet50 DL model was constructed to automatically quantify four categories of imaging biomarkers from chest high-resolution CT (HRCT) and coronary CT angiography images: lung parenchyma (percentage of low attenuation volume, LAV%, 15th percentile density Perc15, mean lung density, MLD), airways (percentage of airway wall thickness WA%, Pi10), pulmonary vasculature (percentage of blood vessels <5 mm in cross-sectional area BV%, vascular fractal dimension), and heart (coronary artery calcium score, CACS, left ventricular mass, LVM, ejection fraction, EF, stroke volume, SV). Concurrently, pulmonary function, cardiopulmonary exercise testing parameters, and six-minute walk distance (6MWD) were assessed at baseline, 6 weeks, and 12 weeks of the intervention. Results: Versus CG, after 12 weeks of intervention, EG demonstrated notable imaging improvements across all GOLD grades: decreased LAV%, increased Perc15 and MLD; reduced WA% and Pi10; increased BV5% and vascular fractal dimension; improved EF and SV, and decreased LVM (all P<0.05). Clinically, EG also showed substantially better FEV1, FVC, peak VO2, and 6MWD than CG (P<0.01). Correlation analysis revealed moderate to strong correlations between these imaging metrics and clinical functional parameters (|r|=0.36–0.62, P<0.001). The constructed DL model demonstrated excellent segmentation accuracy (Dice coefficient: 0.87–0.95) and quantification reliability in both internal and external validations.

Keywords: COPD, CPR exercise training, CT, Deeplearning, Unstable angina pectoris

Received: 30 Oct 2025; Accepted: 21 Jan 2026.

Copyright: © 2026 Xing, Tan, Cheng and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiaoling Cheng

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