Use of three-dimensional printing for simulation in ultrasound education: a scoping review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".