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 wrist is a complex joint involving many small bones and complicated kinematics. It has, therefore, been traditionally difficult to image and ascertain information about kinematics when making a diagnosis. Although MRI and fluoroscopy have been used, they both have limitations. Recently, there has been interest in the use of 4D-CT in imaging the wrist. This review examines the literature regarding the use of 4D-CT in imaging the wrist to assess kinematics and its ability to diagnose pathology. Some questions remain about the description of normal ranges, the most appropriate method of measuring intercarpal stability, the accuracy compared with established standards, and the place of 4D-CT in postoperative assessment. Cite this article:
