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Bone & Joint Open
Vol. 4, Issue 1 | Pages 13 - 18
5 Jan 2023
Walgrave S Oussedik S

Abstract

Robotic-assisted total knee arthroplasty (TKA) has proven higher accuracy, fewer alignment outliers, and improved short-term clinical outcomes when compared to conventional TKA. However, evidence of cost-effectiveness and individual superiority of one system over another is the subject of further research. Despite its growing adoption rate, published results are still limited and comparative studies are scarce. This review compares characteristics and performance of five currently available systems, focusing on the information and feedback each system provides to the surgeon, what the systems allow the surgeon to modify during the operation, and how each system then aids execution of the surgical plan.

Cite this article: Bone Jt Open 2023;4(1):13–18.


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. 103-B, Issue 5 | Pages 822 - 827
1 May 2021
Buzzatti L Keelson B Vanlauwe J Buls N De Mey J Vandemeulebroucke J Cattrysse E Scheerlinck T

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.


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.