3D printing allows for the rapid production of novel 3 dimensional (3D) models. Its use, both for medical  and non-medical purposes, has seen exponential growth in recent years. Including the 3D printing of airways as part of the preassessment process . Within medical education it has already been used for a variety of purposes . Here we explore how it is being used for simulation-based training in airway management and how its use could be further developed.
Pubmed was searched using the terms; 3 dimensional (or 3D) and printing and airway or anaesthesia/anaesthetic and teaching (or training or education). Papers were excluded if their focus was not on airway training, if they were not written in English or did not contain original research. The themes of model creation process and their role for teaching was reviewed.
20 results were returned. However, 13 did not focus on airway management, leaving only 7 results. Models design was created via, de novo design, from pre-existing electronic 3D renders or from cross sectional imaging and then using a computer processing to extract a 3D render which is then optimised before being printed. These cross-sectional images came from either patient specific datasets or from open-source image libraries. Of the papers reviewed the majority were regarding designs for front of neck airway trainers with other uses being for bronchoscopy as well as one paper that was looking at recreating patient specific pathology both for preoperative simulation but also helps in discussions with patients.
These methods provide an interesting opportunity for training. The ease of creating one off components with 3D printing, allowing for the creation of a variety of pathology, seems to be poorly exploited. Instead, most of the work so far has been on 3D printing ‘normal’ airways. There is a significant technical skill required to convert patient anatomy into specific models, which slows adoption of such techniques. Work will also need to be done to assess if these models have greater educational value compared to more traditional models, especially when considering patient specific models for use for pre-procedure practice.
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