Utilizing Three-Dimensional Printing Technology to Assess the Feasibility of High-Fidelity Synthetic Ventricular Septal Defect Models for Simulation in Medical Education
Bibliographic record
Abstract
BACKGROUND: The current educational approach for teaching congenital heart disease (CHD) anatomy to students involves instructional tools and techniques that have significant limitations. This study sought to assess the feasibility of utilizing present-day three-dimensional (3D) printing technology to create high-fidelity synthetic heart models with ventricular septal defect (VSD) lesions and applying these models to a novel, simulation-based educational curriculum for premedical and medical students. METHODS: Archived, de-identified magnetic resonance images of five common VSD subtypes were obtained. These cardiac images were then segmented and built into 3D computer-aided design models using Mimics Innovation Suite software. An Objet500 Connex 3D printer was subsequently utilized to print a high-fidelity heart model for each VSD subtype. Next, a simulation-based educational curriculum using these heart models was developed and implemented in the instruction of 29 premedical and medical students. Assessment of this curriculum was undertaken with Likert-type questionnaires. RESULTS: High-fidelity VSD models were successfully created utilizing magnetic resonance imaging data and 3D printing. Following instruction with these high-fidelity models, all students reported significant improvement in knowledge acquisition (P < .0001), knowledge reporting (P < .0001), and structural conceptualization (P < .0001) of VSDs. CONCLUSIONS: It is feasible to use present-day 3D printing technology to create high-fidelity heart models with complex intracardiac defects. Furthermore, this tool forms the foundation for an innovative, simulation-based educational approach to teach students about CHD and creates a novel opportunity to stimulate their interest in this field.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".