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The Bone & Joint Journal
Vol. 103-B, Issue 9 | Pages 1442 - 1448
1 Sep 2021
McDonnell JM Evans SR McCarthy L Temperley H Waters C Ahern D Cunniffe G Morris S Synnott K Birch N Butler JS

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.


The Bone & Joint Journal
Vol. 97-B, Issue 10_Supple_A | Pages 40 - 44
1 Oct 2015
Thienpont E Lavand'homme P Kehlet H

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: Bone Joint J 2015;97-B(10 Suppl A):40–4.