3D printed ascending aortic simulators with physiological fidelity for surgical simulation
Bibliographic record
Abstract
Introduction: Three-dimensional (3D) printed multimaterial ascending aortic simulators were created to evaluate the ability of polyjet technology to replicate the distensibility of human aortic tissue when perfused at physiological pressures. Methods: Simulators were developed by computer-aided design and 3D printed with a Connex3 Objet500 printer. Two geometries were compared (straight tube and idealised aortic aneurysm) with two different material variants (TangoPlus pure elastic and TangoPlus with VeroWhite embedded fibres). Under physiological pressure, β Stiffness Index was calculated comparing stiffness between our simulators and human ascending aortas. The simulators' material properties were verified by tensile testing to measure the stiffness and energy loss of the printed geometries and composition. Results: The simulators' geometry had no effect on measured β Stiffness Index (p>0.05); however, β Stiffness Index increased significantly in both geometries with the addition of embedded fibres (p<0.001). The simulators with rigid embedded fibres were significantly stiffer than average patient values (41.8±17.0, p<0.001); however, exhibited values that overlapped with the top quartile range of human tissue data suggesting embedding fibres can help replicate pathological human aortic tissue. Biaxial tensile testing showed that fiber-embedded models had significantly higher stiffness and energy loss as compared with models with only elastic material for both tubular and aneurysmal geometries (stiffness: p<0.001; energy loss: p<0.001). The geometry of the aortic simulator did not statistically affect the tensile tested stiffness or energy loss (stiffness: p=0.221; energy loss: p=0.713). Conclusion: We developed dynamic ultrasound-compatible aortic simulators capable of reproducing distensibility of real aortas under physiological pressures. Using 3D printed composites, we are able to tune the stiffness of our simulators which allows us to better represent the stiffness variation seen in human tissue. These models are a step towards achieving better simulator fidelity and have the potential to be effective tools for surgical training.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".