Background There is a significant learning curve when teaching ultrasonography to medical trainees; task trainers can help learners to bridge this gap and develop their skills. Three-dimensional printing technology has the potential to be a great tool in the development of such simulators.
Objective This scoping review aimed to identify what 3D-printed models have been used in ultrasound education to date, how they were created and the pros and limitations involved.
Design Researchers searched three online databases to identify 3D-printed ultrasound models used in medical education.
Results Twelve suitable publications were identified for inclusion in this review. The models from included articles simulated largely low frequency and/or high stakes events, with many models simulating needle guidance procedures. Most models were created by using patient imaging data and a computer-aided design software to print structures directly or print casting molds. The benefits of 3D-printed educational trainers are their low cost, reproducibility, patient specificity and accuracy. The current limitations of this technology are upfront investments and a lack of optimisation of materials.
Conclusions The use of 3D-printed ultrasound task trainers is in its infancy, and more research is needed to determine whether or not this technology will benefit medical learners in the future.
- Simulation Based Education
- Task Trainers And Clinical Skills Centers
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Twitter Gillian Sheppard @gsheppardmd.
Contributors PG and RS initially conceptualised the objectives of the review under the supervision of GS. PG and RS developed the search strategy and completed the literature search. All authors contributed to the development of this manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data sharing not applicable as no datasets generated and/or analysed for this study.
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