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

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

doi:10.3389/fpubh.2022.949366

Cited by 7 publications

(3 citation statements)

References 54 publications

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“…University of California-Los Angeles has a top centrality of 0.18, suggesting that it may play an essential role in the 3D bioprinting field. International cooperation between different institutions is active as well, which is critical to the prosperity of this field ( Feng et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

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Global hotspots and emerging trends in 3D bioprinting research

Ding

Tang

Huang

et al. 2023

Front. Bioeng. Biotechnol.

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Three-dimensional (3D) bioprinting is an advanced tissue engineering technique that has received a lot of interest in the past years. We aimed to highlight the characteristics of articles on 3D bioprinting, especially in terms of research hotspots and focus. Publications related to 3D bioprinting from 2007 to 2022 were acquired from the Web of Science Core Collection database. We have used VOSviewer, CiteSpace, and R-bibliometrix to perform various analyses on 3,327 published articles. The number of annual publications is increasing globally, a trend expected to continue. The United States and China were the most productive countries with the closest cooperation and the most research and development investment funds in this field. Harvard Medical School and Tsinghua University are the top-ranked institutions in the United States and China, respectively. Dr. Anthony Atala and Dr. Ali Khademhosseini, the most productive researchers in 3D bioprinting, may provide cooperation opportunities for interested researchers. Tissue Engineering Part A contributed the largest publication number, while Frontiers in Bioengineering and Biotechnology was the most attractive journal with the most potential. As for the keywords in 3D bioprinting, Bio-ink, Hydrogels (especially GelMA and Gelatin), Scaffold (especially decellularized extracellular matrix), extrusion-based bioprinting, tissue engineering, and in vitro models (organoids particularly) are research hotspots analyzed in the current study. Specifically, the research topics “new bio-ink investigation,” “modification of extrusion-based bioprinting for cell viability and vascularization,” “application of 3D bioprinting in organoids and in vitro model” and “research in personalized and regenerative medicine” were predicted to be hotspots for future research.

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Section: Discussionmentioning

confidence: 99%

“…CiteSpace 6.1. R2 is another bibliometric software for creating visualization maps based on the data retrieved from the database ( Cheng et al 2022 ; Feng et al 2022 ). Several knowledge-based visual maps were constructed, such as citation bursts map, timeline view, and dual-map overlay.…”

Section: Methodsmentioning

confidence: 99%

Global hotspots and emerging trends in 3D bioprinting research

Ding

Tang

Huang

et al. 2023

Front. Bioeng. Biotechnol.

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“…This expands the capabilities of doctors in their practice. The prospect of artificial intelligence medical models is worthy of recognition, and its application scope is constantly expanding (Feng et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm

Zhang,

Ke,

Zhang

et al. 2024

Front. Artif. Intell.

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The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.

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