Basavaraju

MAPPING THE GLOBAL RESEARCH LANDSCAPE OF VOLLEYBALL INJURY EPIDEMIOLOGY AND BIOMECHANICAL RISK FACTORS THROUGH ARTIFICIAL INTELLIGENCE:

A bibliometric analysis (1999–2026)

Basavaraju¹; Nagaraja Y²

Volleyball is a globally popular sport characterized by repetitive jumping, landing, spiking, and blocking actions that increase the risk of musculoskeletal injuries, particularly anterior cruciate ligament (ACL) injuries and other lower-limb disorders. The rapid growth of scientific literature in sports injury epidemiology and biomechanics has created challenges in identifying emerging trends and knowledge structures. This study demonstrates the application of artificial intelligence-assisted bibliometric techniques to map the global research landscape related to volleyball injury epidemiology and biomechanical risk factors from 1999 to 2026. Bibliographic data were retrieved from the Scopus database using keywords related to volleyball, injury, biomechanics, kinematics, and kinetics. Data analysis was performed using the Bibliometrix package and Biblioshiny platform, which employ AI-supported visualization and knowledge-mapping approaches to identify influential publications, authors, journals, thematic clusters, and emerging research directions. The findings reveal a substantial increase in publication output after 2014, reflecting growing scientific interest in injury prevention and biomechanical assessment. Keyword network analysis identified major themes related to knee injuries, ACL risk factors, landing mechanics, and sports injury prevention. Thematic mapping indicated a shift toward evidence-based injury prevention strategies, longitudinal studies, and technology-enhanced biomechanical assessment. Furthermore, AI-assisted bibliometric visualization enabled efficient exploration of complex research networks and facilitated the identification of knowledge gaps and future research priorities. The study highlights the growing role of artificial intelligence in scientific knowledge organization and research evaluation. It demonstrates how AI-supported analytical tools can assist researchers, educators, and policymakers in understanding evolving trends in sports science and promoting data-driven decision-making in education and research.