A review of medical image detection for cancers in digestive system based on artificial intelligence.

Journal: Expert review of medical devices
PMID: 31530047

Abstract

: At present, cancer imaging examination relies mainly on manual reading of doctors, which requests a high standard of doctors' professional skills, clinical experience, and concentration. However, the increasing amount of medical imaging data has brought more and more challenges to radiologists. The detection of digestive system cancer (DSC) based on artificial intelligence (AI) can provide a solution for automatic analysis of medical images and assist doctors to achieve high-precision intelligent diagnosis of cancers. : The main goal of this paper is to introduce the main research methods of the AI based detection of DSC, and provide relevant reference for researchers. Meantime, it summarizes the main problems existing in these methods, and provides better guidance for future research. : The automatic classification, recognition, and segmentation of DSC can be better realized through the methods of machine learning and deep learning, which minimize the internal information of images that are difficult for humans to discover. In the diagnosis of DSC, the use of AI to assist imaging surgeons can achieve cancer detection rapidly and effectively and save doctors' diagnosis time. These can lay the foundation for better clinical diagnosis, treatment planning and accurate quantitative evaluation of DSC.

Authors

  • Jiangchang Xu
    Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University , Shanghai , China.
  • Mengjie Jing
    Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University , Shanghai , China.
  • Shiming Wang
    Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University , Shanghai , China.
  • Cuiping Yang
    Department of Gastroenterology, Ruijin North Hospital of Shanghai Jiao Tong University School of Medicine , Shanghai , China.
  • Xiaojun Chen
    Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.

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