E-101 Endovascular simulation using anatomical-realistic 3D-replicas of pediatric neurovascular pathologies
Bibliographic record
Abstract
Introduction/Purpose Brain vascular anomalies are an important cause of brain injury in the paediatric population. The exposure of operators to endovascular surgeries outside of large pediatric centers is limited, resulting in unfamiliarity and unfortunate exclusion of patients from receiving timely treatments. Furthermore, neuroendovascular surgical procedures are challenging due to the complexity of the diseases and would benefit from presurgical simulation allowing more precise control, reduced complications and better clinical outcomes. Our study introduces a hands-on pediatric-specific endovascular neurosurgery simulator for skills training and treatment planning. Materials and Methods This work is a multidisciplinary collaboration between Neuroradiology, Neurosurgery, MRI physics, Cardiovascular Radiology and Materials Science. Parameters based on neuroimaging were used as inputs to formulate anatomical-realistic 3D-replicas. The following tasks were performed: 1. Image acquisition: Six representative cases with pediatric brain vascular malformations were included in the design phase of the project. Patients underwent 3D-MRI including anatomical and angiographic sequences. These images were used for anatomical rendering of 3D-models. 2. Image segmentation: Anatomical images were manually postprocessed and segmented with the aid of a 3D-image-based engineering software (Mimics/3-Matics by Materialize). 3. Manufacturing development: Based on the initial shape and size, a spectrum of vessel models was generated. Molds were designed around the models using Fused-Deposition Modeling 3D-printing with Acrylonitrile Butadiene Styrene plastic material. 4. Silicone Casting of Phantom Model: Smooth On Dragon Skin 20 silicone was filled into the molds forming the vessel models. The external parts were removed and internal parts chemically dissolved to create a lumen, resulting in a finished anatomical vessel model which replicates the nature of the initial structures. All vascular materials are radiolucent to allow for fluoroscopy, whereas the model-holder mimics anatomical and radiographic properties of the age-specific pediatric skull base and vascular peripheral anatomy. The complete phantom was attached to a pulsatile flow pump which is controlled by an Arduino microcontroller for generating specific pressure/flow rates. Results We developed a pediatric neurovascular simulator from patient-specific MRI anatomy that reproduces the experience of treating pediatric brain pathologies. The simulator materials imitate vascular properties including wall patency, thickness, and elasticity and flow is provided by a high-fidelity pump. The simulator accuracy and feasibility for pediatric endovascular training and presurgical planning was assessed for anatomy, realism, haptics, tactility, and general usage. Conclusion We present a pediatric-specific endovascular neurosurgery simulator using anatomical-realistic 3D-replicas of neurovascular pathologies, with a goal to provide anatomically and hemodynamically accurate training and treatment planning. Disclosures C. Parra-Farinas: None. E. Walsh: None. V. Rea: None. E. Kitamura: None. C. Lam: None. C. Macgowan: None. P. Dirks: None. T. Looi: None. P. Muthusami: None.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".