Artificial Intelligence for Injury Prevention in Iranian Athletes: A Multi-Sport Epidemiological and Machine Learning Review

Sports injuries impose both physical and economic burdens, especially in high-risk and high-participation disciplines. With growing evidence supporting the role of artificial intelligence (AI) in injury prevention, we conducted a systematic review of original studies focused on Iranian athletes. Six sports, football/futsal, volleyball, basketball, martial arts, gymnastics, and handball, were selected based on epidemiological data by Azami-Aghdash et al. (۲۰۱۷). Relevant databases were searched up to ۲۰۲۵, and sixteen studies met the inclusion criteria. The review highlights sport-specific injury patterns, key risk factors, and the predictive capabilities of AI models. Injury rates were highest in football, handball, and martial arts, with lower limb injuries being the most common. AI methods such as decision trees (CART, ADTree), ensemble models (XGBoost, SmooteBoost), deep learning (CNN), and logistic regression were applied using neuromuscular, biomechanical, anthropometric, and genetic data. Several models achieved high predictive accuracy, with AUC values up to ۰.۸۳۷ and accuracy rates exceeding ۸۰%. These findings underscore the promise of AI in advancing injury prediction and prevention strategies in Iranian athletes.

کلیدواژه ها:

Artificial Intelligence ، Injury Prevention ، Iran ، futsal and futball ، volleyball ، basketball ، martial Arts ، gymnastics