Application of AI tool in early prediction of secondary pulmonary hypertension in patients with COPD

Corresponding author: mayila·abudukelimu, 1925531959@qq.com

DOI: 10.12201/bmr.202607.00013

•  

  Abstract: Chronic obstructive pulmonary disease (COPD) is frequently complicated by pulmonary hypertension (PH) during its progression, which substantially increases the risk of right heart failure and mortality, thereby adversely affecting patient prognosis. Currently available clinical examination methods fall short of achieving precise prediction, and there is an urgent need for more effective novel tools in clinical practice. This review discusses the application of artificial intelligence (AI) techniques for early prediction of COPD?PH, encompassing automated quantification of imaging features based on convolutional neural networks (CNNs), construction of multimodal data fusion models, risk stratification, and theoretical underpinnings for prognostic assessment. Studies have demonstrated that AI models achieve higher predictive accuracy than traditional approaches, and Shapley Additive exPlanations (SHAP) are increasingly being integrated into model development. Nevertheless, most studies still face notable limitations, including a lack of external prospective validation, significant data heterogeneity, and suboptimal multimodal fusion, which collectively undermine the clinical utility of these predictive methods. Future research should prioritize prospective multicenter validation, federated learning approaches, and development of explainable AI techniques to facilitate the clinical translation of AI tools for early COPD?PH prediction.

  Key words: Chronic Obstructive Pulmonary Disease; Pulmonary Hypertension; Artificial Intelligence; Early Prediction; Radiomics; Deep Learning

  Submit time: 7 July 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• Chen Cuiyan, Liang Huiying. Development of an Ensemble Screening Model for Chronic Obstructive Pulmonary Disease Using Multimodal Data. 2026. doi: 10.12201/bmr.202604.00086

  Hongying Ma. Advances in Exhaled Breath Condensate for Chronic Obstructive Pulmonary Disease. 2025. doi: 10.12201/bmr.202512.00038

  zhoujianguo, hufangyun. Progress in CT AI diagnosis of pulmonary nodules. 2025. doi: 10.12201/bmr.202504.00068

  Wang Nan, Chen Zhen. Correlation between the Expression of Peripheral Blood miR-145-5p and miR-223 and Treatment Response and Prognosis in Patients with Chronic Obstructive Pulmonary Disease Complicated with Pulmonary Tuberculosis. 2026. doi: 10.12201/bmr.202604.00163

  LI LinHao, WANG Kun, LI Peng, XU XiaoChen, HAN Ping. Study on Chronic Obstructive Pulmonary Disease Complicated with Malnutrition Based on the TCM Theory of Child Consumes Qi of Mother. 2026. doi: 10.12201/bmr.202601.00090

  孟. Advances in deep learning-based Artificial Intelligence techniques in gastrointestinal stromal tumors.. 2024. doi: 10.12201/bmr.202411.00057

  Zhou Jie², Xiao Wei², Sun Zengtao. Understanding the Mechanism and Therapeutic Strategies of the Gut-Lung Axis in Chronic Obstructive Pulmonary Disease from the Perspective of. 2026. doi: 10.12201/bmr.202601.00005

  Liao Siqing, Zhou Xia. Management of chronic pulmonary aspergillosis with hemoptysis. 2025. doi: 10.12201/bmr.202512.00043

  YangHuiQin. . 2026. doi: 10.12201/bmr.202605.00078

  TANG BAO XIN, GAO YILI, Dong Yanfen, Xu Chuan, Wang Yujie, Wang Fangfang, Ni Ting, Shen Shunyi, Zhao Chunyan. Application Status and Strategy Consideration of Artificial Intelligence in Abnormal Lipid Management for Patients with premature coronary artery disease. 2026. doi: 10.12201/bmr.202604.00036

• ID	Submit time	Number	Download
  1	2026-05-18	10.12201/bmr.202607.00013V1	Download

• Public  Anonymous  To author only Submit Comments