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人工智能助力上肢关节磁共振成像的进展:综述

Artificial intelligence powered advancements in upper extremity joint MRI: A review.

作者信息

Chen Wei, Lim Lincoln Jian Rong, Lim Rebecca Qian Ru, Yi Zhe, Huang Jiaxing, He Jia, Yang Ge, Liu Bo

机构信息

Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.

Department of Medical Imaging, Western Health, Footscray Hospital, Victoria, Australia.

出版信息

Heliyon. 2024 Mar 25;10(7):e28731. doi: 10.1016/j.heliyon.2024.e28731. eCollection 2024 Apr 15.

Abstract

Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.

摘要

磁共振成像(MRI)是肌肉骨骼医学中不可或缺的医学成像检查技术。现代MRI技术可实现对软组织和骨骼病变的高质量多平面成像,且无电离辐射的有害影响。目前MRI的一些局限性包括采集时间长、伪影和噪声。此外,区分具有相似信号特征的相邻或紧密贴合的软组织结构往往具有挑战性。在过去十年中,人工智能(AI)已广泛应用于肌肉骨骼MRI,以帮助缩短图像采集时间并提高图像质量。除了能够降低医疗成本外,AI还可以协助临床医生更准确地诊断疾病。这将有效地帮助制定适当的治疗方案,并最终改善患者护理。这篇综述文章旨在总结AI目前在肌肉骨骼MRI中的研究和应用,特别是深度学习(DL)在识别MRI图像中上肢关节结构和病变方面的进展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d973/11002577/14ca6ce29020/gr1.jpg

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