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.

Abstract

Nerve transfer has become a common and often effective reconstructive strategy for proximal and complex peripheral nerve injuries of the upper limb. This case-based discussion explores the principles and potential benefits of nerve transfer surgery and offers in-depth discussion of several established and valuable techniques including: motor transfer for elbow flexion after musculocutaneous nerve injury, deltoid reanimation for axillary nerve palsy, intrinsic re-innervation following proximal ulnar nerve repair, and critical sensory recovery despite non-reconstructable median nerve lesions.

Given the growing prevalence of obesity around the world and its association with osteoarthritis of the knee, orthopaedic surgeons need to be familiar with the management of the obese patient with degenerative knee pain. The precise mechanism by which obesity leads to osteoarthritis remains unknown, but is likely to be due to a combination of mechanical, humoral and genetic factors.

Weight loss has clear medical benefits for the obese patient and seems to be a logical way of relieving joint pain associated with degenerative arthritis. There are a variety of ways in which this may be done including diet and exercise, and treatment with drugs and bariatric surgery. Whether substantial weight loss can delay or even reverse the symptoms associated with osteoarthritis remains to be seen.

Surgery for osteoarthritis in the obese patient can be technically more challenging and carries a risk of additional complications. Substantial weight loss before undertaking total knee replacement is advisable. More prospective studies that evaluate the effect of significant weight loss on the evolution of symptomatic osteoarthritis of the knee are needed so that orthopaedic surgeons can treat this patient group appropriately.