The Use of 3D Printed Vasculature for Simulation-based Medical Education Within Interventional Radiology
Bibliographic record
Abstract
Three-dimensional (3D) printing has become a useful tool within the field of medicine as a way to produce custom anatomical models for teaching, surgical planning, and patient education. This technology is quickly becoming a key component in simulation-based medical education (SBME) to teach hands-on spatial perception and tactile feedback. Within fields such as interventional radiology (IR), this approach to SBME is also thought to be an ideal instructional method, providing an accurate and economical means to study human anatomy and vasculature. Such anatomical details can be extracted from patient-specific and anonymized CT or MRI scans for the purpose of teaching or analyzing patient-specific anatomy. There is evidence that 3D printing in IR can also optimize procedural training, so learners can rehearse procedures under fluoroscopy while receiving immediate supervisory feedback. Such training advancements in IR hold the potential to reduce procedural operating time, thus reducing the amount of time a patient is exposed to radiation and anaesthetia. Using a program evaluation approach, the purpose of this technical report is to describe the development and application of 3D-printed vasculature models within a radiology interest group to determine their efficacy as supplementary learning tools to traditional, lecture-based teaching. The study involved 30 medical students of varying years in their education, involved in the interest group at Memorial University of Newfoundland (MUN). The session was one hour in length and began with a Powerpoint presentation demonstrating the insertion of guide wires and stents using 3D-printed vasculature models. Participants had the opportunity to use the models to attempt several procedures demonstrated during the lecture. These attempts were supervised by an educational expert/facilitator. A survey was completed by all 30 undergraduate medical students and returned to the facilitators, who compiled the quantitative data to evaluate the efficacy of the 3D-printed models as an adjunct to the traditional didactic teaching within IR. The majority of feedback was positive, supporting the use of 3D=printed vasculature as an additional tactile training method for medical students within an IR academic setting. The hands-on experience provides a valuable training approach, with more opportunities for the rehearsal of high-acuity, low-occurrence (HALO) procedures performed in IR.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".