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:
Total knee arthroplasty (TKA) is a major orthopaedic
intervention. The length of a patient's stay has been progressively
reduced with the introduction of enhanced recovery protocols: day-case
surgery has become the ultimate challenge. This narrative review shows the potential limitations of day-case
TKA. These constraints may be social, linked to patient’s comorbidities,
or due to surgery-related adverse events (e.g. pain, post-operative
nausea and vomiting, etc.). Using patient stratification, tailored surgical techniques and
multimodal opioid-sparing analgesia, day-case TKA might be achievable
in a limited group of patients. The younger, male patient without
comorbidities and with an excellent social network around him might
be a candidate. Demographic changes, effective recovery programmes and less invasive
surgical techniques such as unicondylar knee arthroplasty, may increase
the size of the group of potential day-case patients. The cost reduction achieved by day-case TKA needs to be balanced
against any increase in morbidity and mortality and the cost of
advanced follow-up at a distance with new technology. These factors
need to be evaluated before adopting this ultimate ‘fast-track’
approach. Cite this article:
