Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{190757,
        author = {DHANUSH R KALKUR and MANOJ S PATIL and MOHAMMAD AMAN and MOHAMMAD ANAS},
        title = {An AI-Driven Framework for Early Sports Injury Prediction Using Time-Series Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4993-5000},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190757},
        abstract = {Sports injuries pose a significant challenge in competitive and professional athletics, often leading to reduced performance, disrupted training schedules, and long-term health consequences for athletes. Predicting injuries in advance is inherently complex due to the dynamic, multivariate, and time-dependent nature of physiological and performance-related data. This paper presents an AI-driven framework for early sports injury prediction using time-series deep learning techniques. The proposed system analyzes sequential athlete data, including training load, intensity variations, workload accumulation, biomechanical indicators, and historical injury records, to identify latent patterns associated with elevated injury risk. Deep learning models capable of capturing temporal dependencies are employed to learn complex relationships within longitudinal data and generate proactive risk assessments. The framework is designed to support continuous monitoring and early warning generation, enabling timely intervention through training adjustments and preventive strategies. Experimental evaluation demonstrates that the proposed approach effectively identifies injury-prone patterns earlier than conventional threshold-based methods. The results highlight the potential of time-series deep learning to enhance injury prevention, athlete safety, and performance sustainability through data-driven decision support in sports science and sports medicine applications.},
        keywords = {Sports Injury Prediction; Time-Series Analysis; Deep Learning; Athlete Monitoring; Injury Prevention; Predictive Analytics; Sports Analytics; Artificial Intelligence in Sports},
        month = {January},
        }