Injuries to the hamstring muscle complex are common in athletes, accounting for between 12% and 26% of all injuries sustained during sporting activities. Acute hamstring injuries often occur during sports that involve repetitive kicking or high-speed sprinting, such as American football, soccer, rugby, and athletics. They are also common in watersports, including waterskiing and surfing. Hamstring injuries can be career-threatening in elite athletes and are associated with an estimated risk of recurrence in between 14% and 63% of patients. The variability in prognosis and treatment of the different injury patterns highlights the importance of prompt diagnosis with
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:
Primary total knee arthroplasty (TKA) is a reliable
procedure with reproducible long-term results. Nevertheless, there
are conditions related to the type of patient or local conditions
of the knee that can make it a difficult procedure. The most common
scenarios that make it difficult are discussed in this review. These
include patients with many previous operations and incisions, and
those with severe coronal deformities, genu recurvatum, a stiff knee,
extra-articular deformities and those who have previously undergone
osteotomy around the knee and those with chronic dislocation of
the patella. Each condition is analysed according to the characteristics of
the patient, the pre-operative planning and the reported outcomes. When approaching the difficult primary TKA surgeons should use
a systematic approach, which begins with the review of the existing
literature for each specific clinical situation. Cite this article:
