E-101 Endovascular simulation using anatomical-realistic 3D-replicas of pediatric neurovascular pathologies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».