人工智能驱动的分析表明，前交叉韧带重建、髋关节镜检查与股骨髋臼撞击综合征以及肩关节不稳是关节镜领域发表最为频繁的主题。

Artificial Intelligence-Driven Analysis Identifies Anterior Cruciate Ligament Reconstruction, Hip Arthroscopy and Femoroacetabular Impingement Syndrome, and Shoulder Instability as the Most Commonly Published Topics in Arthroscopy.

作者信息

Baird Henry B G, Allen William, Gallegos Mauricio, Ashy Cody C, Slone Harris S, Pullen W Michael

机构信息

College of Medicine, Medical University of South Carolina, Charleston, South Carolina, U.S.A.

Department of Orthopaedics and Physical Medicine, Medical University of South Carolina, Charleston, South Carolina, U.S.A.

出版信息

Arthrosc Sports Med Rehabil. 2025 Feb 21;7(3):101108. doi: 10.1016/j.asmr.2025.101108. eCollection 2025 Jun.

DOI:10.1016/j.asmr.2025.101108

PMID:40692938

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12276563/

Abstract

PURPOSE

To use advanced topic modeling, specifically the Bidirectional Encoder Representations from Transformers Topic (BERTopic) Model, to analyze research topics in .

METHODS

Text data from the titles and abstracts of 7,304 original articles and reviews published in between 1985 and 2023 were included to train the BERTopic artificial intelligence (AI) model for topic generation. BERTopic, an advanced natural language processing tool implemented in Python via Jupyter Notebook, uses contextual embeddings and clustering algorithms to efficiently group large datasets into topics based on semantic similarity. The AI-generated topics were then analyzed by frequency (i.e., the number of studies classified under each topic from 1985 to 2023) and popularity (i.e., "hot" and "cold" topic patterns based on linear regression models of topic frequency from 2020 to 2023).

RESULTS

The BERTopic model categorized 6,901 articles into 35 topics. The most common topics from 1985 to 2023 were anterior cruciate ligament reconstruction, hip arthroscopy and femoroacetabular impingement (FAI), and shoulder instability. From 2020 to 2023, hip arthroscopy and femoroacetabular impingement, superior capsular reconstruction, and anterior cruciate ligament reconstruction were identified as "hot" or popular topics, whereas suture anchor biomechanics, platelet-rich plasma, and arthroscopic irrigation were identified as "cold" topics, indicating a decline in popularity.

CONCLUSIONS

Using BERTopic, the study showed an efficient way to analyze large amounts of data to establish patterns within orthopaedic sports medicine literature. This study shows the capacity of the BERTopic model to synthesize thousands of articles within into 35 key topics. The ability to process large amounts of data with accuracy and efficiency provides a powerful tool for establishing and defining the current landscape and potential future directions of orthopaedic literature.

CLINICAL RELEVANCE

Using AI to investigate topics a journal has published will allow us to recognize patterns, identifying common topics, emerging topics, and shifts in focus over time. It will also allow us to identify research gaps that may need to be addressed.

摘要

目的

运用先进的主题建模方法，特别是来自Transformer的双向编码器表示主题（BERTopic）模型，分析[具体领域]的研究主题。

方法

纳入1985年至2023年间发表的7304篇原创文章和综述的标题及摘要中的文本数据，用于训练BERTopic人工智能（AI）模型以生成主题。BERTopic是一种通过Jupyter Notebook在Python中实现的先进自然语言处理工具，它使用上下文嵌入和聚类算法，根据语义相似性将大型数据集有效地分组为主题。然后，通过频率（即1985年至2023年每个主题下分类的研究数量）和受欢迎程度（即基于2020年至2023年主题频率的线性回归模型的“热门”和“冷门”主题模式）对AI生成的主题进行分析。

结果

BERTopic模型将6901篇文章分类为35个主题。1985年至2023年最常见的主题是前交叉韧带重建、髋关节镜检查和股骨髋臼撞击症（FAI）以及肩关节不稳。2020年至2023年，髋关节镜检查和股骨髋臼撞击症、上盂唇重建以及前交叉韧带重建被确定为“热门”或受欢迎的主题，而缝线锚钉生物力学、富血小板血浆和关节镜冲洗被确定为“冷门”主题，表明受欢迎程度下降。