There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
Acute bone and joint infections in children are serious, and misdiagnosis can threaten limb and life. Most young children who present acutely with pain, limping, and/or loss of function have transient synovitis, which will resolve spontaneously within a few days. A minority will have a bone or joint infection. Clinicians are faced with a diagnostic challenge: children with transient synovitis can safely be sent home, but children with bone and joint infection require urgent treatment to avoid complications. Clinicians often respond to this challenge by using a series of rudimentary decision support tools, based on clinical, haematological, and biochemical parameters, to differentiate childhood osteoarticular infection from other diagnoses. However, these tools were developed without methodological expertise in diagnostic accuracy and do not consider the importance of imaging (ultrasound scan and MRI). There is wide variation in clinical practice with regard to the indications, choice, sequence, and timing of imaging. This variation is most likely due to the lack of evidence concerning the role of imaging in acute bone and joint infection in children. We describe the first steps of a large UK multicentre study, funded by the National Institute for Health Research, which seeks to integrate definitively the role of imaging into a decision support tool, developed with the assistance of individuals with expertise in the development of clinical
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
The anterior cruciate ligament (ACL) is frequently injured in elite athletes, with females up to eight times more likely to suffer an ACL tear than males. Biomechanical and hormonal factors have been thoroughly investigated; however, there remain unknown factors that need investigation. The mechanism of injury differs between males and females, and anatomical differences contribute significantly to the increased risk in females. Hormonal factors, both endogenous and exogenous, play a role in ACL laxity and may modify the risk of injury. However, data are still limited, and research involving oral contraceptives is potentially associated with methodological and ethical problems. Such characteristics can also influence the outcome after ACL reconstruction, with higher failure rates in females linked to a smaller diameter of the graft, especially in athletes aged < 21 years. The addition of a lateral extra-articular tenodesis can improve the outcomes after ACL reconstruction and reduce the risk of failure, and it should be routinely considered in young elite athletes. Sex-specific environmental differences can also contribute to the increased risk of injury, with more limited access to and availablility of advanced training facilities for female athletes. In addition, football kits are designed for male players, and increased attention should be focused on improving the quality of pitches, as female leagues usually play the day after male leagues. The kit, including boots, the length of studs, and the footballs themselves, should be tailored to the needs and body shapes of female athletes. Specific physiotherapy programmes and training protocols have yielded remarkable results in reducing the risk of injury, and these should be extended to school-age athletes. Finally, psychological factors should not be overlooked, with females’ greater fear of re-injury and lack of confidence in their knee compromising their return to sport after ACL injury. Both intrinsic and extrinsic factors should be recognized and addressed to optimize the training programmes which are designed to prevent injury, and improve our understanding of these injuries. Cite this article:
Tennis elbow (lateral epicondylitis or lateral elbow tendinopathy) is a self-limiting condition in most patients. Surgery is often offered to patients who fail to improve with conservative treatment. However, there is no evidence to support the superiority of surgery over continued nonoperative care or no treatment. New evidence also suggests that the prognosis of tennis elbow is not influenced by the duration of symptoms, and that there is a 50% probability of recovery every three to four months. This finding challenges the belief that failed nonoperative care is an indication for surgery. In this annotation, we discuss the clinical and research implications of the benign clinical course of tennis elbow. Cite this article:
Economic evaluation provides a framework for assessing the costs and consequences of alternative programmes or interventions. One common vehicle for economic evaluations in the healthcare context is the decision-analytic model, which synthesizes information on parameter inputs (for example, probabilities or costs of clinical events or health states) from multiple sources and requires application of mathematical techniques, usually within a software program. A plethora of decision-analytic modelling-based economic evaluations of orthopaedic interventions have been published in recent years. This annotation outlines a number of issues that can help readers, reviewers, and decision-makers interpret evidence from decision-analytic modelling-based economic evaluations of orthopaedic interventions. Cite this article:
This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets. Cite this article:
