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Peer-reviewedCC BY-NC-NDQuantitative imaging in medicine and surgery

A deep learning-based model combining quantitative parameters of initial chest computed tomography and clinical variables for the prediction of posttraumatic acute respiratory distress syndrome

  • Wenzhao Zhang1
  • Xu Hu2
  • Ke Ning1
  • Huayan Xu3
  • Jianqun Yu1
  1. 1Department of Radiology , West China Hospital , Sichuan University , Chengdu , ;
  2. 2Department of Emergency , West China Hospital , Sichuan University , Chengdu ,
  3. 3Department of Radiology , West China Second University Hospital , Chengdu , ;
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Cite

BibTeXRIS
BibTeX
@article{zhang2026deep,
  title = {{A deep learning-based model combining quantitative parameters of initial chest computed tomography and clinical variables for the prediction of posttraumatic acute respiratory distress syndrome}},
  author = {Wenzhao Zhang and Xu Hu and Ke Ning and Huayan Xu and Jianqun Yu},
  journal = {Quantitative imaging in medicine and surgery},
  year = {2026},
  month = aug,
  url = {https://europepmc.org/article/PMC/PMC13654174},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - A deep learning-based model combining quantitative parameters of initial chest computed tomography and clinical variables for the prediction of posttraumatic acute respiratory distress syndrome
AU  - Wenzhao Zhang
AU  - Xu Hu
AU  - Ke Ning
AU  - Huayan Xu
AU  - Jianqun Yu
PY  - 2026
DA  - 2026/08/30
JO  - Quantitative imaging in medicine and surgery
UR  - https://europepmc.org/article/PMC/PMC13654174
SN  - 2223-4292
SN  - 2223-4306
ER  -