Effectiveness of Artificial Intelligence in Early Detection of Shoulder Girdle Injuries in Professional Athletes: A Multicenter Study

Shoulder girdle injuries in professional athletes often lead to prolonged recovery and decreased performance, highlighting the critical need for early and accurate diagnosis. This study aims to evaluate the effectiveness of artificial intelligence (AI) technologies in the early identification of such injuries to improve clinical outcomes and reduce reinjury rates. Employing a multicenter design, data were collected from diverse sports medicine centers involving 312 professional athletes undergoing routine screening and injury assessment. Advanced AI algorithms, including convolutional neural networks and machine learning classifiers, were applied to imaging data and biomechanical patterns for precise injury detection. Statistical analysis using receiver operating characteristic curve (ROC) and area under the curve (AUC) metrics demonstrated AI models achieved up to 92% sensitivity and 88% specificity in early injury detection. Furthermore, AI integration enabled a 23% reduction in reinjury rates compared to conventional diagnostic methods. These results confirm that AI-driven approaches provide superior diagnostic accuracy and timely intervention opportunities, facilitating individualized rehabilitation protocols. The novelty of this research lies in the successful implementation of AI across multiple centers with diverse athlete populations, validating its broad applicability. The findings support incorporating AI technology into routine sports medicine practice to enhance injury prevention and optimize athlete health. Future studies should explore real-time AI monitoring and personalized risk prediction models to further advance shoulder injury management.