Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer.

Journal: Chinese medical journal
Published Date: Dec 5, 2019

Abstract

BACKGROUND: Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features. This study aimed to use deep neural networks for computed tomography (CT) diagnosis of perigastric metastatic lymph nodes (PGMLNs) to simulate the recognition of lymph nodes by radiologists, and to acquire more accurate identification results.

Authors

  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Zheng-Dong Zhang
    State Key Laboratory of Virtual Reality Technology & Systems, Beihang University, Beijing 100191, China.
  • Shuo Li
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yu-Ting Guo
    State Key Laboratory of Virtual Reality Technology & Systems, Beihang University, Beijing 100191, China.
  • Qing-Yao Wu
    Department of General Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266555, China.
  • Shu-Hao Liu
    Department of General Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266555, China.
  • Shu-Jian Yang
    Department of General Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266555, China.
  • Lei Ding
    Shandong Key Laboratory of Digital Medicine & Computer Assisted Surgery, Qingdao University, Qingdao, Shandong 266003, China.
  • Bao-Chun Zhao
    Department of Follow-up, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266003, China.
  • Shuai Li
    School of Molecular Biosciences, Center for Reproductive Biology, College of Veterinary Medicine, Washington State University.
  • Yun Lu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.

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