Despite being one of the most common injuries around the elbow, the optimal treatment of olecranon fractures is far from established and stimulates debate among both general orthopaedic trauma surgeons and upper limb specialists. It is almost universally accepted that stable non-displaced fractures can be safely treated nonoperatively with minimal specialist input. Internal fixation is recommended for the vast majority of displaced fractures, with a range of techniques and implants to choose from. However, there is concern regarding the complication rates, largely related to symptomatic metalwork resulting in high rates of implant removal. As the number of elderly patients sustaining these injuries increases, we are becoming more aware of the issues associated with fixation in osteoporotic bone and the often fragile soft-tissue envelope in this group. Given this, there is evidence to support an increasing role for nonoperative management in this high-risk demographic group, even in those presenting with displaced and/or multifragmentary fracture patterns. This review summarizes the available literature to date, focusing predominantly on the management techniques and available implants for stable fractures of the olecranon. It also offers some insights into the potential avenues for future research, in the hope of addressing some of the pertinent questions that remain unanswered. 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:
