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

Overview

Journal Magn Reson Med Sci

Specialty Radiology

Date 2023 Aug 2

PMID 37532584

Authors

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

Affiliations

Soon will be listed here.

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.

Citing Articles

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