Paediatric bone sarcomas are a dual challenge for orthopaedic surgeons in terms of tumour resection and reconstruction, as it is important to minimize functional and growth problems without compromising survival rates. Cañadell’s technique consists of a Type I epiphysiolysis performed using continuous distraction by an external fixator prior to resection. It was designed to achieve a safe margin due to the ability of the physeal cartilage to be a barrier to tumour spread in some situations, avoiding the need for articular reconstruction, and preserving the growth capacity most of the times. Despite initial doubts raised in the scientific community, this technique is now widely used in many countries for the treatment of metaphyseal paediatric bone sarcomas. This annotation highlights the importance of Cañadell’s work and reviews the experience of applying it to bone sarcoma patients over the last 40 years. 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:
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
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:
Radiological imaging is necessary in a wide variety
of trauma and elective orthopaedic operations. The evolving orthopaedic
workforce includes an increasing number of pregnant workers. Current
legislation in the United Kingdom, Europe and United States allows
them to choose their degree of participation, if any, with fluoroscopic procedures.
For those who wish to engage in radiation-prone procedures, specific
regulations apply to limit the radiation dose to the pregnant worker
and unborn child. This paper considers those aspects of radiation protection, the
potential effects of exposure to radiation in pregnancy and the
dose of radiation from common orthopaedic procedures, which are
important for safe clinical practice.
