Exploring trends and key topics in anterior lumbar interbody fusion surgery: A medical text analysis approach

Background: Anterior lumbar interbody fusion (ALIF) is a widely adopted technique for managing lumbar degenerative disorders. However, comprehensive analyses of its research trends, technological developments, and public engagement are still lacking. Objective: This study examines the research landscape, public perception, and innovation trends in ALIF, while also evaluating data extraction methods for managing ALIF-related information. Methods: ALIF-related data from Web of Science, YouTube, and Lens were analyzed. Bibliometric analysis explored international collaborations, key authors, and leading journals. Artificial intelligence models were used to extract information on surgical techniques, diseases, and evaluation metrics. Sentiment analysis categorized YouTube comments as positive, negative, or neutral, while patent data were assessed based on jurisdiction, application type, and legal status. Results: A total of 660 publications, 1311 YouTube comments, and 53 patents were identified. Scientific output increased, peaking in 2021, with the United States (USA) leading in collaborations and author contributions. Neurosurgeons accounted for most ALIF-related publications. World Neurosurgery published the highest number of ALIF articles among top journals from 2017 to 2023. Patents were largely focused on ALIF implants, with half still active. YouTube comments peaked in 2022, with sentiment analysis showing 41.6% positive responses, 36.2% neutral, and 22.2% negative responses. Conclusions: ALIF research has grown significantly in academic output, technological innovation, and public engagement. The USA, China, and South Korea are major contributors, maintaining strong collaborations. Neurosurgeons possess extensive experience in ALIF surgical techniques, and the interest in ALIF-related articles in WORLD NEUROSURGERY has been progressively increasing. Artificial intelligence models demonstrated effectiveness in extracting surgical data, though challenges remain with disease-related information. The rise of ALIF-related YouTube content and sentiment analysis findings highlight growing public interest, with generally positive perceptions.