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:
Dislocation following total hip arthroplasty (THA) is a well-known and potentially devastating complication. Clinicians have used many strategies in attempts to prevent dislocation since the introduction of THA. While the importance of postoperative care cannot be ignored, particular emphasis has been placed on preoperative planning in the prevention of dislocation. The strategies have progressed from more traditional approaches, including modular implants, the size of the femoral head, and augmentation of the offset, to newer concepts, including patient-specific component positioning combined with computer navigation, robotics, and the use of dual-mobility implants. As clinicians continue to pursue improved outcomes and reduced complications, these concepts will lay the foundation for future innovation in THA and ultimately improved outcomes. Cite this article:
