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: Bone Joint J 2021;103-B(9):1442–1448.

Evaluating musculoskeletal conditions of the lower limb and understanding the pathophysiology of complex bone kinematics is challenging. Static images do not take into account the dynamic component of relative bone motion and muscle activation. Fluoroscopy and dynamic MRI have important limitations. Dynamic CT (4D-CT) is an emerging alternative that combines high spatial and temporal resolution, with an increased availability in clinical practice. 4D-CT allows simultaneous visualization of bone morphology and joint kinematics. This unique combination makes it an ideal tool to evaluate functional disorders of the musculoskeletal system. In the lower limb, 4D-CT has been used to diagnose femoroacetabular impingement, patellofemoral, ankle and subtalar joint instability, or reduced range of motion. 4D-CT has also been used to demonstrate the effect of surgery, mainly on patellar instability. 4D-CT will need further research and validation before it can be widely used in clinical practice. We believe, however, it is here to stay, and will become a reference in the diagnosis of lower limb conditions and the evaluation of treatment options.

Cite this article: Bone Joint J 2021;103-B(5):822–827.

Evidence suggests that the alleviation of pain is enhancedby a strong patient-clinician relationship and attending to a patient’s social and mental health. There is a limited role for medication, opioids in particular.

Orthopaedic surgeons can use comprehensive biopsychosocial strategies to help people recover and can work with colleagues who have the appropriate expertise in order to maximize pain alleviation with optimal opioid stewardship.

Preparing patients for elective surgery and caring for them after unplanned injury or surgery can benefit from planned and practiced strategies based in communication science.

Cite this article: Bone Joint J 2020;102-B(9):1122–1127.

Continuous technical improvement in spinal surgical procedures, with the aim of enhancing patient outcomes, can be assisted by the deployment of advanced technologies including navigation, intraoperative CT imaging, and surgical robots. The latest generation of robotic surgical systems allows the simultaneous application of a range of digital features that provide the surgeon with an improved view of the surgical field, often through a narrow portal.

There is emerging evidence that procedure-related complications and intraoperative blood loss can be reduced if the new technologies are used by appropriately trained surgeons. Acceptance of the role of surgical robots has increased in recent years among a number of surgical specialities including general surgery, neurosurgery, and orthopaedic surgeons performing major joint arthroplasty. However, ethical challenges have emerged with the rollout of these innovations, such as ensuring surgeon competence in the use of surgical robotics and avoiding financial conflicts of interest. Therefore, it is essential that trainees aspiring to become spinal surgeons as well as established spinal specialists should develop the necessary skills to use robotic technology safely and effectively and understand the ethical framework within which the technology is introduced.

Traditional and more recently developed platforms exist to aid skill acquisition and surgical training which are described.

The aim of this narrative review is to describe the role of surgical robotics in spinal surgery, describe measures of proficiency, and present the range of training platforms that institutions can use to ensure they employ confident spine surgeons adequately prepared for the era of robotic spinal surgery.

Cite this article: Bone Joint J 2020;102-B(5):568–572.

Aims

The diagnosis of periprosthetic joint infection can be difficult due to the high rate of culture-negative infections. The aim of this study was to assess the use of next-generation sequencing for detecting organisms in synovial fluid.

‘Big data’ is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Billions of dollars have been spent on attempts to build predictive tools from large sets of poorly controlled healthcare metadata. Companies often sell reports at a physician or facility level based on various flawed data sources, and comparative websites of ‘publicly reported data’ purport to educate the public. Physicians should be aware of concerns and pitfalls seen in such data definitions, data clarity, data relevance, data sources and data cleaning when evaluating analytic reports from metadata in health care.

Cite this article: Bone Joint J 2017;99-B:1571–6.

Previous standards for assessing the reliability of a measurement tool have lacked consistency. We reviewed the most current American Society for Testing and Materials and International Organisation for Standardisation (ISO) recommendations, and propose an algorithm for orthopaedic surgeons. When assessing a measurement tool, conditions of the experimental set-up and clear formulae used to compile the results should be strictly reported. According to these recent guidelines, accuracy is a confusing word with an overly broad meaning and should therefore be abandoned. Depending on the experimental conditions, one should be referring to bias (when the study protocol involves accepted reference values), and repeatability (sr, r) or reproducibility (SR, R). In the absence of accepted reference values, only repeatability (sr, r) or reproducibility (SR, R) should be provided.

Take home message: Assessing the reliability of a measurement tool involves reporting bias, repeatability and/or reproducibility depending on the defined conditions, instead of precision or accuracy.

Cite this article: Bone Joint J 2016;98-B2:166–72.