Role of AI and Machine Learning in Predicting Risk of Reinjury After ACL Reconstruction

سال انتشار: 1404
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 156

متن کامل این مقاله منتشر نشده است و فقط به صورت چکیده یا چکیده مبسوط در پایگاه موجود می باشد.
توضیح: معمولا کلیه مقالاتی که کمتر از ۵ صفحه باشند در پایگاه سیویلیکا اصل مقاله (فول تکست) محسوب نمی شوند و فقط کاربران عضو بدون کسر اعتبار می توانند فایل آنها را دریافت نمایند.

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

AIMS02_435

تاریخ نمایه سازی: 29 تیر 1404

چکیده مقاله:

Background and Aims: The application of Artificial Intelligence (AI) and Machine Learning (ML) in sports medicine—particularly before, during, and after surgery—has gained significant momentum in the management of Anterior Cruciate Ligament (ACL) injuries. Among these, AI plays a pivotal role in predicting and managing ACL injuries, which are common among athletes and pose substantial challenges in recovery, especially due to the high risk of reinjury within the first year post-surgery. Methods: AI and ML techniques are utilized to analyze large datasets comprising biomechanical assessments, physical performance metrics, and medical imaging. Supervised learning algorithms such as Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNN) have been employed to predict reinjury risk with high accuracy. These models are trained using data from motion analysis, muscle strength tests, and MRI scans to enhance diagnostic precision and predictive capabilities. Results: In the post-surgical phase, AI contributes significantly to optimizing rehabilitation protocols. By analyzing data from wearable sensors and motion capture systems, AI models assess knee stability, muscle strength, and movement asymmetry to provide personalized training programs that reduce the risk of reinjury. Conclusion: Despite challenges such as data quality, model interpretability, and the need for extensive validation, AI and ML have shown remarkable impact in improving patient outcomes, diagnostic accuracy, and rehabilitation processes for ACL injuries. Current studies mainly rely on controlled datasets, which may not reflect real-world conditions, and often overlook psychological factors such as athletes' mental readiness. Nonetheless, with further research and validation, these technologies are poised to become an integral part of sports medicine, shaping a new future in ACL injury prevention and recovery.

کلیدواژه ها:

نویسندگان

Melika Barazandeh Far

Student Research Committee, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

Ali Ashiani

Student Research Committee, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

Fatemeh Hosseinzadeh Khankahi

Student Research Committee, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran