A Real-World Clinical Study on Computed Tomography Pulmonary Angiography-Based Artificial Intelligence-Driven Risk Stratification in Pulmonary Embolism.

Authors

Zhujun Li, Jie Zheng, Shutong Yang, Baoping Zhang, Wei Hou, Qian Tian, Shumeng Zhu, Yao Xiao, Wenzhe Zhao, Xin Huang, Chao Jin, Zhijie Jian, Ying Wei, Feng Shi, Jian Yang, Zhe Liu

Published in

Journal of thoracic imaging. Oct 14, 2026. Epub Oct 14, 2026.

Abstract

To evaluate the efficacy of a computed tomography pulmonary angiography (CTPA)-based artificial intelligence system for use in the clinical setting to stratify the risk of pulmonary embolism (PE).
This study enrolled 115 patients diagnosed with PE based on positive CTPA reports at our institution between January 1 and October 10, 2023. Cases were classified as relatively high-risk or low-risk according to the 2019 ESC Guidelines. Two radiologists independently and blindly measured risk-related imaging parameters, including main pulmonary artery diameter (MPAD), ascending aortic diameter (AAD), right and left ventricular diameters (RVD, LVD), the right-to-left ventricular diameter ratio (RVD/LVD ratio), pulmonary artery obstruction index (PAOI), and pulmonary embolism volume (PEV). These results were compared with those from the automated artificial intelligence (AI) system to assess consistency.
The agreement (intraclass correlation coefficients, ICCs) of MPAD, AAD, RVD, PAOI, LVD, and RVD/LVD ratio between AI system and manual measurements were 0.94, 0.79, 0.78, 0.80, 0.54, and 0.48, respectively. Among the AI-extracted covariates, an RVD/LVD ratio >1.2 showed good performance in identifying relatively high-risk PE cases, with an area under the curve (AUC) of 0.771. The AI model incorporating all covariates achieved an AUC of 0.848 for classifying high-risk PE.
The CTPA-based AI system can rapidly extract the key imaging parameters relevant to PE risk stratification in real-world clinical settings, significantly reducing the CTPA interpretation time and improving evaluation efficiency.

PMID:
42870456
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.