The Iterative Design and Development of an Affordable Ultrasound Simulator
Bibliographic record
Abstract
Simulation-based medical education (SBME) offers a secure and controlled environment for training in ultrasound-related clinical skills such as nerve blocking and intravenous cannulation. Sonographer training for point-of-care ultrasound often adopts the train-the-trainer (TTT) model, wherein a select group of sonographers receive on-site training to subsequently instruct others. This model traditionally relies on expensive commercial ultrasound simulators, which presents a barrier to the scale-up of the TTT model. This study aims to address the need for cost-effective ultrasound simulators suitable for both initial and cascaded TTT. The objective of this report is to present the design and development of an affordable ultrasound simulator, which mimics anatomical features under ultrasound. The simulator was created using additive manufacturing techniques, including 3D printing, ballistic gel, and silicone work. We report on three development-feedback iterations, with feedback provided by an experienced sonographer from FUJIFILM Sonosite Canada Inc. using the think-aloud approach. Overall the results indicate that de-gassed silicone may serve as a good medium; vessels are best produced as hollow canals within the de-gassed silicone; 3D-printed bones cast acoustic shadows, which are reduced by increasing rigidity of the structures, and 3D printing filament and silicone can be used for nerve bundles. Future developments will focus on achieving anatomical accuracy, exploring alternative materials and printing parameters for the bones, and analyzing embedded structures at varying depths within the silicone. The next steps involve integrating the simulator into ultrasound curricula for a formal assessment of its effectiveness as a training tool.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".