Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.

Journal: Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Published Date:

Abstract

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

Authors

  • Noriyuki Fujima
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA.
  • Koji Kamagata
  • 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.
  • Shohei Fujita
    Department of Radiology, Juntendo University School of Medicine.
  • Yasutaka Fushimi
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.
  • Masahiro Yanagawa
    Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
  • Rintaro Ito
    Department of Innovative Biomedical Visualization, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan.
  • Takahiro Tsuboyama
    From the Department of Radiology, Osaka University Graduate School of Medicine.
  • Mariko Kawamura
    Department of Radiology, Nagoya University Graduate School of Medicine.
  • 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.
  • Akira Yamada
    Department of Radiology, Shinshu University School of Medicine, Japan.
  • Taiki Nozaki
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan.
  • Tomoyuki Fujioka
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • 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.
  • 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.
  • Shinji Naganawa
    Department of Radiology, Nagoya University Graduate School of Medicine.