A 20-year research trend analysis of the artificial intelligence on scoliosis using bibliometric methods.

Background: This bibliometric analysis aimed to map the knowledge network of artificial intelligence in scoliosis.

Methods: Studies on artificial intelligence published from January 2003 to December 2024 are retrieved from Web of Science Core Collection (WoSCC). The contributions of countries, institutions, authors, and journals are identified using VOSviewer, Online Analysis Platform of Literature Metrology (http://biblimetric.com) and Microsoft Excel. Tendencies, hotspots and knowledge networks are analyzed and visualized using VOS-viewer and CiteSpace.

Results: 718 publications are included in the final analysis. The leading country in this field is China. Royal Hospital for Sick Children featured the highest number of publications among all institutions and National University of Singapore featured the highest citations of publications. Co-citation cluster labels revealed characteristics of three main clusters: (1) Image process and classification of scoliosis, (2) AI application in surgical treatment of scoliosis, (3) predict postoperative complications and scoliosis development. Keyword burst detection indicated that machine learning and deep learning are the newly emerging research hot spots.

Conclusion: This study compiled 718 publications covering AI in scoliosis and showed that the direction of these studies is likely in transition from cerebral palsy to machine learning and deep learning. It provides guidance for further research and clinical applications on AI application in scoliosis.