Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging

被引:5
|
作者
Nakaura, Takeshi [1 ,2 ]
Kobayashi, Naoki [1 ]
Yoshida, Naofumi [1 ]
Shiraishi, Kaori [1 ]
Uetani, Hiroyuki [1 ]
Nagayama, Yasunori [1 ]
Kidoh, Masafumi [1 ]
Hirai, Toshinori [1 ]
机构
[1] Kumamoto Univ, Grad Sch Med Sci, Dept Diagnost Radiol, Kumamoto, Kumamoto, Japan
[2] Kumamoto Univ Hosp, Radiol, 1-1-1 Honjo,Chuo Ku, Kumamoto, Kumamoto 8608556, Japan
关键词
artificial intelligence; deep learning; machine learning; magnetic resonance imaging; CONVOLUTIONAL NEURAL-NETWORK; RADIOMICS; APPROXIMATE; TUMOR; MODEL;
D O I
10.2463/mrms.rev.2022-0102
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponen-tially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.
引用
收藏
页码:147 / 156
页数:10
相关论文
共 50 条
  • [31] Imaging: Artificial Intelligence in Medical Imaging
    Schaeffer, Colin
    Leon, Stephanie
    [J]. MEDICAL PHYSICS, 2021, 48 (06)
  • [32] Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers
    Calderaro, Julien
    Kather, Jakob Nikolas
    [J]. GUT, 2021, 70 (06) : 1183 - 1193
  • [33] Application of artificial intelligence to hepatobiliary cancer clinical outcomes research
    Endo, Yutaka
    Alaimo, Laura
    Catalano, Giovanni
    Chatzipanagiotou, Odysseas P.
    Pawlik, Timothy M.
    [J]. ARTIFICIAL INTELLIGENCE SURGERY, 2024, 4 (02): : 59 - 67
  • [34] The Emerging Role of Automation, Measurement Standardization, and Artificial Intelligence in Foot and Ankle Imaging: An Update
    Ghandour, Samir
    Ashkani-Esfahani, Soheil
    Kwon, John Y.
    [J]. FOOT AND ANKLE CLINICS, 2023, 28 (03) : 667 - 680
  • [35] Automated segmentation of the human placenta and uterus with MR imaging using artificial intelligence (AI)
    Twickler, Diane M.
    Do, Quyen N.
    Xi, Yin
    Shahedi, Maysam
    Dormer, James
    Devi, Anusha T. T.
    Lewis, Matthew A.
    Spong, Catherine Y.
    Dashe, Jodi S.
    Madhuranthakam, Ananth
    Fei, Baowei
    [J]. AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY, 2020, 222 (01) : S158 - S159
  • [36] HEPATOBILIARY IMAGING AND THE USE OF INTRAVENOUS MORPHINE
    KESLAR, PJ
    TURBINER, EH
    [J]. CLINICAL NUCLEAR MEDICINE, 1987, 12 (08) : 592 - 596
  • [37] Imaging Advances in Stroke: Use of Advanced Neurovascular Imaging or Disruptive Innovation With Artificial Intelligence?
    Liebeskind, David S.
    Wardlaw, Joanna M.
    [J]. STROKE, 2023, 54 (04) : 1123 - 1126
  • [38] Artificial Intelligence in Intracoronary Imaging
    Russell Fedewa
    Rishi Puri
    Eitan Fleischman
    Juhwan Lee
    David Prabhu
    David L. Wilson
    D. Geoffrey Vince
    Aaron Fleischman
    [J]. Current Cardiology Reports, 2020, 22
  • [39] Artificial intelligence in molecular imaging
    Herskovits, Edward H.
    [J]. ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (09)
  • [40] Artificial Intelligence in Medical Imaging
    Wagner, Jessyca B.
    [J]. RADIOLOGIC TECHNOLOGY, 2019, 90 (05) : 489 - 501