Quantitative assessment and risk stratification of random acute pulmonary embolism cases using a deep learning model based on computed tomography pulmonary angiography images
The VB-Net DL model based on CTPA could conveniently and efficiently detect and quantitatively evaluate PE.
Authors
- Yaozong Gao
- Xiaodan Ye (ORCID: https://orcid.org/0000-0003-3059-2740)
- Yanbo Chen (ORCID: https://orcid.org/0000-0003-4210-0598)
- Mengsu Zeng (ORCID: https://orcid.org/0000-0002-0299-9407)
- Qiao Yang (ORCID: https://orcid.org/0000-0002-7698-796X)
- Cheng Yan (ORCID: https://orcid.org/0000-0001-5139-2735)
Institutions
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- United Imaging Healthcare (China) (CN)
- Shanghai Institute of Hematology (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-03-01
- DOI
- https://doi.org/10.21037/qims-24-1412
- Citations
- 3
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 5.61