The Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, China.
Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital of Central South University, Changsha, China.
Front Public Health. 2022 Jul 19;10:949366. doi: 10.3389/fpubh.2022.949366. eCollection 2022.
As a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.
We screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.
A total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.
Publications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.
深度学习作为一个研究热点，不断与医学中的各个研究领域相结合。最近，骨科中基于深度学习的研究越来越多。本研究旨在通过文献计量学分析确定近年来深度学习在骨科中的应用热点，并推断未来的研究趋势。
我们通过访问 Web of Science 核心合集，筛选了全球范围内关于深度学习在骨科中应用的文献。这些文章和综述没有语言和时间限制。Citespace 用于对出版物进行文献计量学分析。
共检索到 822 篇文章和综述。分析表明，基于年度出版物，深度学习在骨科中的应用具有广阔的发展前景。发文最多的国家是美国，其次是中国。加州大学旧金山分校和骨骼放射学是最有影响力的机构和期刊，分别位列第一和第二。引用最多的作者是 LeCun Y，其发表文章的期刊影响因子最高。目前的热门关键词是卷积神经网络、分类、分割、诊断、图像、骨折和骨关节炎。突现关键词是风险因素、识别、定位和手术。时间线视图显示了骨肿瘤和骨质疏松症的两个近期研究方向。
近年来，关于深度学习在骨科中的应用的文献数量有所增加，美国是发文最多的国家。目前的研究主要集中在从医学图像中对骨关节炎和骨折进行分类、诊断和风险预测。未来的研究方向可能侧重于降低术中风险、预测术后并发症的发生、骨质疏松症的筛查以及常规影像学中骨肿瘤的识别和分类。
