Recent trends in AI applications for pelvic MRI: a comprehensive review.

Journal: La Radiologia medica
Published Date: Aug 3, 2024

Abstract

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Authors

  • Takahiro Tsuboyama
    From the Department of Radiology, Osaka University Graduate School of Medicine.
  • Masahiro Yanagawa
    Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
  • Tomoyuki Fujioka
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Shohei Fujita
    Department of Radiology, Juntendo University School of Medicine.
  • Daiju Ueda
    Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan. ai.labo.ocu@gmail.com.
  • Rintaro Ito
    Department of Innovative Biomedical Visualization, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan.
  • Akira Yamada
    Department of Radiology, Shinshu University School of Medicine, Japan.
  • Yasutaka Fushimi
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.
  • Fuminari Tatsugami
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Takeshi Nakaura
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.). Electronic address: kff00712@nifty.com.
  • Taiki Nozaki
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan.
  • Koji Kamagata
  • Yusuke Matsui
    Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan.
  • Kenji Hirata
    Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
  • Noriyuki Fujima
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA.
  • Mariko Kawamura
    Department of Radiology, Nagoya University Graduate School of Medicine.
  • Shinji Naganawa
    Department of Radiology, Nagoya University Graduate School of Medicine.

Keywords

External Resources

Popular Topics
Recent Journals