Sorin Vera, Barash Yiftach, Konen Eli, Klang Eyal
Department of Diagnostic Imaging, Chaim Sheba Medical Center, affiliated to the Sackler School of Medicine, Tel-Aviv University, Israel.
Department of Diagnostic Imaging, Chaim Sheba Medical Center, affiliated to the Sackler School of Medicine, Tel-Aviv University, Israel.
J Am Coll Radiol. 2020 May;17(5):639-648. doi: 10.1016/j.jacr.2019.12.026. Epub 2020 Jan 28.
Natural language processing (NLP) enables conversion of free text into structured data. Recent innovations in deep learning technology provide improved NLP performance. We aimed to survey deep learning NLP fundamentals and review radiology-related research.
This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched for deep learning NLP radiology studies published up to September 2019. MEDLINE, Scopus, and Google Scholar were used as search databases.
Ten relevant studies published between 2018 and 2019 were identified. Deep learning models applied for NLP in radiology are convolutional neural networks, recurrent neural networks, long short-term memory networks, and attention networks. Deep learning NLP applications in radiology include flagging of diagnoses such as pulmonary embolisms and fractures, labeling follow-up recommendations, and automatic selection of imaging protocols. Deep learning NLP models perform as well as or better than traditional NLP models.
Research and use of deep learning NLP in radiology is increasing. Acquaintance with this technology can help prepare radiologists for the coming changes in their field.
自然语言处理（NLP）可将自由文本转换为结构化数据。深度学习技术的最新创新提升了NLP的性能。我们旨在调查深度学习NLP的基本原理并综述与放射学相关的研究。
本系统评价按照系统评价和Meta分析的首选报告项目指南进行报告。我们检索了截至2019年9月发表的深度学习NLP放射学研究。使用MEDLINE、Scopus和谷歌学术作为检索数据库。
确定了2018年至2019年间发表的10项相关研究。应用于放射学NLP的深度学习模型有卷积神经网络、循环神经网络、长短期记忆网络和注意力网络。深度学习NLP在放射学中的应用包括标记肺栓塞和骨折等诊断、标注随访建议以及自动选择成像方案。深度学习NLP模型的表现与传统NLP模型相当或更好。
深度学习NLP在放射学中的研究和应用正在增加。熟悉这项技术有助于放射科医生为其所在领域即将到来的变化做好准备。
