MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia.

Journal: European radiology
PMID: 38231392

Abstract

OBJECTIVE: To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant correlation of each clinical feature in determining these two types of pneumonia using a random forest dichotomous diagnosis model. This will enhance diagnostic accuracy and efficiency in distinguishing between SAP and ASP.

Authors

  • Tong Liu
    Intensive Care Medical Center, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, People's Republic of China.
  • Zheng-Hua Zhang
    Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 650032, People's Republic of China.
  • Qi-Hao Zhou
    School of Information, Yunnan University, Kunming, Yunnan, 650032, People's Republic of China.
  • Qing-Zhao Cheng
    The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China.
  • Yue Yang
    Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China.
  • Jia-Shu Li
    The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China.
  • Xue-Mei Zhang
    Department of Medical and Nursing, The Affiliated Rehabilitation Hospital of Chongqing Medical University, Chongqing 400050.
  • Jian-Qing Zhang
    The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China. ydyyzjq@163.com.

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