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. 2022 Jul 19:10:949366.
doi: 10.3389/fpubh.2022.949366. eCollection 2022.

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

Chengyao Feng  1   2 , Xiaowen Zhou  3 , Hua Wang  3 , Yu He  4 , Zhihong Li  1   2 , Chao Tu  1   2
Affiliations

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

Chengyao Feng et al. Front Public Health. .

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 orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.

Methods: 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.

Results: 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 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 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.

Keywords: Citespace; bibliometric analysis; deep learning; orthopedics; research trends.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Number and trend of annual publications.
Figure 2
Figure 2
Map of countries (or regions) cooperation networks (A) and institution cooperation networks (B). The nodes represent country (or region) or institution. The lines represent cooperation relationships. The colors in the nodes represent the years, and the purple ring represents centrality.
Figure 3
Figure 3
Map of author's cooperative relationship (A) and co-citation network (B). The nodes represent author or co-cited author, and the lines represent cooperation or co-citation relationships, respectively. The colors in the nodes represent the years, and the purple ring represents centrality.
Figure 4
Figure 4
Map of journal co-citation and cited references. (A) The nodes represent journal. The lines represent co-citation relationships. The colors in the nodes represent the years, and the purple ring represents centrality. (B) The nodes represent cited reference. The lines represent co-citation relationships. The colors in the nodes represent the years, and the purple ring represents centrality.
Figure 5
Figure 5
Map of keywords occurrence (A) and the clustering of keywords (B). For keywords occurrence, the nodes represent keywords. The lines represent co-occurrence relationships, and the colors in the nodes represent the years.
Figure 6
Figure 6
The top 30 keywords with the strongest citation bursts.
Figure 7
Figure 7
The timeline view of keywords.
See this image and copyright information in PMC

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