Triplane ankle fractures are complex injuries typically occurring in children aged between 12 and 15 years. Classic teaching that closure of the physis dictates the overall fracture pattern, based on studies in the 1960s, has not been challenged. The aim of this paper is to analyze whether these injuries correlate with the advancing closure of the physis with age. A fracture mapping study was performed in 83 paediatric patients with a triplane ankle fracture treated in three trauma centres between January 2010 and June 2020. Patients aged younger than 18 years who had CT scans available were included. An independent Paediatric Orthopaedic Trauma Surgeon assessed all CT scans and classified the injuries as n-part triplane fractures. Qualitative analysis of the fracture pattern was performed using the modified Cole fracture mapping technique. The maps were assessed for both patterns and correlation with the closing of the physis until consensus was reached by a panel of six surgeons.Aims
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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 number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS).Aims
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