AUTHOR=Feng Chengyao , Zhou Xiaowen , Wang Hua , He Yu , Li Zhihong , Tu Chao 

TITLE=Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.949366

DOI=10.3389/fpubh.2022.949366

ISSN=2296-2565

ABSTRACT=Background: 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 orthopaedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopaedics in recent years and infer future research trends. 
Methods: We screened global publication on deep learning applications in orthopaedics 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.
Results: A total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopaedics 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 Nature 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. 
Conclusion: Publications on deep learning applications in orthopaedics 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.