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:
The timing of surgical fixation in spinal fractures is a contentious topic. Existing literature suggests that early stabilization leads to reduced morbidity, improved neurological outcomes, and shorter hospital stay. However, the quality of evidence is low and equivocal with regard to the safety of early fixation in the severely injured patient. This paper compares complication profiles between spinal fractures treated with early fixation and those treated with late fixation. All patients transferred to a national tertiary spinal referral centre for primary surgical fixation of unstable spinal injuries without preoperative neurological deficit between 1 July 2016 and 20 October 2017 were eligible for inclusion. Data were collected retrospectively. Patients were divided into early and late cohorts based on timing from initial trauma to first spinal operation. Early fixation was defined as within 72 hours, and late fixation beyond 72 hours.Aims
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