Artificial intelligence in medical imaging of the liver

被引:0
|
作者
Li-Qiang Zhou [1 ]
Jia-Yu Wang [1 ]
Song-Yuan Yu [2 ]
Ge-Ge Wu [1 ]
Qi Wei [1 ]
You-Bin Deng [1 ]
Xing-Long Wu [3 ]
Xin-Wu Cui [1 ]
Christoph F Dietrich [1 ,4 ]
机构
[1] Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
[2] Department of Ultrasound, Tianyou Hospital Affiliated to Wuhan University of Technology
[3] School of Mathematics and Computer Science, Wuhan Textitle University
[4] Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg
关键词
Liver; Imaging; Ultrasound; Artificial intelligence; Machine learning; Deep learning;
D O I
暂无
中图分类号
TP18 [人工智能理论]; R575 [肝及胆疾病];
学科分类号
081104 ; 0812 ; 0835 ; 1002 ; 100201 ; 1405 ;
摘要
Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.
引用
收藏
页码:672 / 682
页数:11
相关论文
共 50 条
  • [1] Artificial intelligence in medical imaging of the liver
    Zhou, Li-Qiang
    Wang, Jia-Yu
    Yu, Song-Yuan
    Wu, Ge-Ge
    Wei, Qi
    Deng, You-Bin
    Wu, Xing-Long
    Cui, Xin-Wu
    Dietrich, Christoph F.
    [J]. WORLD JOURNAL OF GASTROENTEROLOGY, 2019, 25 (06) : 672 - 682
  • [2] Imaging: Artificial Intelligence in Medical Imaging
    Schaeffer, Colin
    Leon, Stephanie
    [J]. MEDICAL PHYSICS, 2021, 48 (06)
  • [3] Artificial Intelligence in Medical Imaging
    Wagner, Jessyca B.
    [J]. RADIOLOGIC TECHNOLOGY, 2019, 90 (05) : 489 - 501
  • [4] Artificial intelligence and medical imaging
    Sun, Roger
    Deutsch, Eric
    Fournier, Laure
    [J]. BULLETIN DU CANCER, 2022, 109 (01) : 83 - 88
  • [5] Artificial intelligence in medical imaging
    Gore, John C.
    [J]. MAGNETIC RESONANCE IMAGING, 2020, 68 : A1 - A4
  • [6] Trustworthy Artificial Intelligence in Medical Imaging
    Hasani, Navid
    Morris, Michael A.
    Rhamim, Arman
    Summers, Ronald M.
    Jones, Elizabeth
    Siegel, Eliot
    Saboury, Babak
    [J]. PET CLINICS, 2022, 17 (01) : 1 - 12
  • [7] Artificial Intelligence in Medical Imaging of the Breast
    Lei, Yu-Meng
    Yin, Miao
    Yu, Mei-Hui
    Yu, Jing
    Zeng, Shu-E
    Lv, Wen-Zhi
    Li, Jun
    Ye, Hua-Rong
    Cui, Xin-Wu
    Dietrich, Christoph F.
    [J]. FRONTIERS IN ONCOLOGY, 2021, 11
  • [8] Artificial Intelligence in cardiovascular medical imaging
    Stanciu, Silviu
    Tache, Irina A.
    Gurzun, Magdalena
    Sorici, Alexandru
    Croitoru, Alexandru
    Cuzino, Dragos
    Tudor, Diana L.
    Lazara, Sorin
    [J]. ROMANIAN JOURNAL OF MILITARY MEDICINE, 2020, 123 (04) : 310 - 316
  • [9] Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor
    Nishida, Naoshi
    Kudo, Masatoshi
    [J]. FRONTIERS IN ONCOLOGY, 2020, 10
  • [10] Artificial intelligence, digital and medical imaging professions
    Laredo, J-D
    [J]. BULLETIN DE L ACADEMIE NATIONALE DE MEDECINE, 2022, 206 (08): : 1126 - 1127