人工智能在脊柱畸形领域研究热点的可视化分析.

BACKGROUND: With the continuous improvement and progress of artificial intelligence technology in the treatment of spinal deformity, a large number of studies have been invested in this field, but the main research status, hot spots and development trends are still unclear. OBJECTIVE: To visually analyze the literature related to artificial intelligence in the field of spinal deformities by using bibliometrics, identify the research hotspots and shortcomings in this field, and provide references for future research. METHODS: The core database of Web of Science was used to search the articles related to artificial intelligence in the field of spinal deformities published from inception to 2023. The data were visually analyzed by Citespace 5.6.R5 and VOSviewer 1.6.19. RESULTS AND CONCLUSION: (1) A total of 165 papers were included, and the number of papers published in this field showed a fluctuating upward trend. The author with the largest number of articles is Lafage V, and the country with the largest number of articles is China. (2) Keyword analysis results show that adolescent scoliosis, deep learning, classification, precision and robot are the main keywords. (3) The in-depth analysis results of co-cited and highly cited articles show that artificial intelligence has three hotspots in the field of spinal deformities, including the use of U-shaped architecture (a mature mode of deep learning convolutional neural networks) to automatically measure imaging parameters (Cobb angle and accurate segmentation of paraspinal muscles), multiview correlation network architecture (i.e., spine curvature assessment framework), and robot-guided spinal surgery. (4) In the field of artificial intelligence treatment of spinal malformations, the mechanism research such as genomics is very weak. In the future, unsupervised hierarchical clustering and other machine learning techniques can be used to study the basic mechanism of susceptibility genes in the field of spinal deformities by genome-wide association analysis and other genomics research methods. [ABSTRACT FROM AUTHOR]