Peer-reviewed article
Building smarter surveillance: a framework for artificial intelligence-enabled monitoring of overuse injuries in youth sports
Patrick Mayolo, Zachary A Blashinsky, John Wahl, Alexander Bouterse, Felix Oettl et al. ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This paper looks at early sport specialization, meaning intensive year-round training in one sport, and its link to overuse injuries and dropping out of sport among young athletes. It notes that wearable sensors and artificial intelligence can measure training load, movement problems and recovery, but that current AI injury models were mostly tested on elite adult men. The authors propose a four-pillar framework for AI-based injury monitoring designed for youth sport. The framework is presented as a roadmap that could help guide prevention and support long-term bone and muscle health.
Key points
- Early sport specialization is increasingly common among youth athletes.
- This trend has coincided with more overuse injuries and early attrition from sport.
- Existing AI surveillance models are mostly validated in elite adult male groups, leaving youth athletes underserved.
- The authors propose a four-pillar framework for AI injury monitoring that accounts for early sport specialization.
- The framework links technology with developmental physiology as a risk-monitoring roadmap.
Limitations
The abstract describes a proposed conceptual framework and does not report any testing with young athletes or data on whether it reduces injuries. The countries listed come from metadata, and the abstract does not say which countries the work covers.
Abstract
Early sport specialization (ESS), defined as intensive, year-round training in a single sport at the exclusion of others, has become increasingly prevalent among youth athletes. This trend has coincided with a rise in overuse injuries, and premature attrition. Concurrently, advances in wearable sensors and artificial intelligence (AI) enable real-time quantification of training load, biomechanical deviations, and recovery. However, a translational gap remains: existing AI surveillance models are predominantly validated in elite adult male cohorts, leaving youth athletes underserved. To address this gap, we propose a translational four-pillar framework for ESS-aware AI injury monitoring in youth sport. Bridging technological capabilities with developmental physiology through structured AI surveillance provides a structured risk-monitoring roadmap with the potential to inform preventative strategies, optimize physical readiness, and support long-term musculoskeletal health in youth athletes.