著者

Noriyuki Fujima Koji Kamagata Daiju Ueda Shohei Fujita Yasutaka Fushimi Masahiro Yanagawa Rintaro Ito Takahiro Tsuboyama Mariko Kawamura Takeshi Nakaura Akira Yamada Taiki Nozaki Tomoyuki Fujioka Yusuke Matsui Kenji Hirata Fuminari Tatsugami Shinji Naganawa

出版者

Japanese Society for Magnetic Resonance in Medicine

雑誌

Magnetic Resonance in Medical Sciences (ISSN:13473182)

巻号頁・発行日

pp.rev.2023-0047, (Released:2023-08-01)

参考文献数

123

被引用文献数

4

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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.