Anterior cruciate ligament (ACL) injuries are among the most common and debilitating knee injuries in professional athletes with an incidence in females up to eight-times higher than their male counterparts. ACL injuries can be career-threatening and are associated with increased risk of developing knee osteoarthritis in future life. The increased risk of ACL injury in females has been attributed to various anatomical, developmental, neuromuscular, and hormonal factors. Anatomical and hormonal factors have been identified and investigated as significant contributors including osseous anatomy, ligament laxity, and hamstring muscular recruitment. Postural stability and impact absorption are associated with the stabilizing effort and stress on the ACL during sport activity, increasing the risk of noncontact pivot injury. Female patients have smaller diameter hamstring autografts than males, which may predispose to increased risk of re-rupture following ACL reconstruction and to an increased risk of chondral and meniscal injuries. The addition of an extra-articular tenodesis can reduce the risk of failure; therefore, it should routinely be considered in young elite athletes. Prevention programs target key aspects of training including plyometrics, strengthening, balance, endurance and stability, and neuromuscular training, reducing the risk of ACL injuries in female athletes by up to 90%. Sex disparities in access to training facilities may also play an important role in the risk of ACL injuries between males and females. Similarly, football boots, pitches quality, and football size and weight should be considered and tailored around females’ characteristics. Finally, high levels of personal and sport-related stress have been shown to increase the risk of ACL injury which may be related to alterations in attention and coordination, together with increased muscular tension, and compromise the return to sport after ACL injury. Further investigations are still necessary to better understand and address the risk factors involved in ACL injuries in female athletes. Cite this article:
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article:
