AI and ACL Injury Prevention: The Future of Sports Medicine?
As an athlete, one of the injuries we hate to hear about most is an ACL tear. ACL injuries can lead to months of rehabilitation, loss of playing time, and in severe cases, even ending an athlete's career. Despite all the advancements in sports medicine, ACL injuries still pose a significant threat to athletes at all levels. However, recent developments in AI are bringing new hope to the world of sports medicine, and researchers have been able to create predictive models to identify athletes at high risk of ACL injuries.
The normal biomechanical function of the ACL is influenced by multiple anatomic factors, such as an increased lateral tibial posterior slope, smaller ACL diameter, a narrow femoral intercondylar notch, LMH, leg-length discrepancies, and the valgus alignment of the lower extremities. Identifying these factors before an injury can be difficult, and the sensitivity and specificity of isolated measurements are usually limited. However, AI can help in identifying the ideal combination of variables with their corresponding cutoff points.
Several ACL injury-prediction algorithms have been described in recent years. These algorithms use laboratory-based tools to determine predictive mechanisms that underlie increased knee abduction moment during landing, clinic-based measurements and computer analysis, transverse plane hip net moment impulse, asymmetrical sagittal plane knee moment at initial contact, 2-dimensional frontal plane knee excursion, kinesthesia, and balance, and involved limb postural stability deficits.
In orthopedics, AI has already demonstrated an accuracy above 96% in the diagnosis of complete ACL tears. Moreover, a decision-support model was used to aid differentiation between partial ACL injuries and complete ACL tears using knee MRI scans. This technology has been used to create a novel predictive model for primary ACL injury with a more than 90% accuracy. This model relies on only two variables that can be measured easily before the injury and can be obtained from a standard MRI scan of the knee.
This model could be used as a screening tool to identify high-risk patients, one that could give them the opportunity to follow prevention measures such as the use of knee braces, lifestyle modifications, and the implementation of ACL prevention programs. Athletes can also benefit from this technology by using this information to train more intelligently and reduce the risk of ACL injury.
The future of sports medicine looks bright, and AI is at the forefront of this new era. Predictive models like the one described above have potential to decrease the risk of ACL injuries and improve the overall health and well-being of athletes at all levels. With AI, sports medicine is not only becoming more accurate but also more accessible, allowing for more widespread use and improved outcomes. Stay tuned and be prepared for the new era of AI in sports medicine.
