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 prediction tools. Cite this article:
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:
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:
Advances in hip arthroscopy have renewed interest in the ligamentum teres. Considered by many to be a developmental vestige, it is now recognised as a significant potential source of pain and mechanical symptoms arising from the hip joint. Despite improvements in imaging, arthroscopy remains the optimum method of diagnosing lesions of the ligamentum teres. Several biological or mechanical roles have been proposed for the ligament. Unless these are disproved, the use of surgical procedures that sacrifice the ligamentum teres, as in surgical dislocation of the hip, should be carefully considered. This paper provides an update on the development, structure and function of the ligamentum teres, and discusses associated clinical implications.
