Name: Basavaraju & Nagaraja Y
Country: India

Year: 2026
Volume: Volume-16, Issue-1 (Special Issue: March 2026)
Page Number: 14-23
DOI: https://doi.org/10.5281/zenodo.19145859

ABSTRACT
Background
Youth volleyball athletes are frequently exposed to repetitive jump-landing and cutting
movements that substantially increase the risk of non-contact anterior cruciate ligament (ACL) injury. Biomechanical factors such as dynamic knee valgus, elevated vertical ground reaction force (GRF), and reduced hip flexion have been consistently associated with ACL injury mechanisms. Artificial intelligence (AI) has emerged as a promising tool for injury risk prediction; however, the effectiveness of multimodal AI frameworks integrating biomechanical and contextual variables in youth volleyball remains insufficiently synthesised.

Objective:
To systematically review and meta-analyze the evidence on multimodal artificial intelligence models incorporating biomechanical and complementary predictors for injury risk classification in youth volleyball athletes.

Methods:
A systematic review and meta-analysis were conducted in accordance with PRISMA
guidelines. PubMed, Scopu