Aims. The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Methods. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data. Results. With an associated area under the receiver-operator curve ranging between 0.75 and 0.98, the optimized ML models resulted in good to excellent predictions. The best performing model used a random forest approach while considering both alignment and intra-articular load readings. Conclusion. The presented model has the potential to make experience available to surgeons adopting new technology, bringing expert opinion in their operating theatre, but also provides insight in the surgical decision process. More specifically, these promising outcomes indicated the relevance of considering the overall limb alignment in the coronal and sagittal plane to identify the appropriate surgical decision

Aims

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds.

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The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.

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Patient-reported outcome measures have become an important part of routine care. The aim of this study was to determine if Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be used to create patient subgroups for individuals seeking orthopaedic care.

We describe the development and validation of a scoring system for auditing orthopaedic surgery. It is a minor modification of the POSSUM scoring system widely used in general surgery. The orthopaedic POSSUM system which we have developed gives predictions for mortality and morbidity which correlate well with the observed rates in a sample of 2326 orthopaedic operations over a period of 12 months

In order to assess current opinions on the long-term outcome after primary total hip replacement, we performed a multicentre, cross-sectional survey in 22 centres from 12 European countries. Different patient characteristics were categorised into ‘decreases chances’, ‘does not affect chances’, and ‘increases chances’ of a favourable long-term outcome, by 304 orthopaedic surgeons and 314 referring practitioners. The latter were less likely to associate age older than 80 years and obesity with a favourable outcome than orthopaedic surgeons (p < 0.001 and p = 0.006, respectively) and more likely to associate age younger than 50 years with a favourable outcome (p = 0.006). Comorbidity, rheumatoid arthritis, and poor bone quality were thought to be associated with a decreased chance of a favourable outcome. We found important differences in the opinions regarding long-term outcome after total hip replacement within and between referring practitioners and orthopaedic surgeons. These are likely to affect access to and the provision of total hip replacement.