Biomodex patient-specific brain aneurysm models: the value of simulation for first in-human experiences using new devices and robotics
Bibliographic record
Abstract
BACKGROUND: With the recent advent of advanced technologies in the field, treatment of neurovascular diseases using endovascular techniques is rapidly evolving. Here we describe our experience with pre-surgical simulation using the Biomodex EVIAS patient-specific 3D-printed models to plan aneurysm treatment using endovascular robotics and novel flow diverter devices. METHODS: Pre-procedural rehearsals with 3D-printed patient-specific models of eight cases harboring brain aneurysms were performed before the first in-human experiences. To assess the reliability of the experimental model, the characteristics of the aneurysms were compared between the patient and 3D models. The rehearsals were used to define the patient treatment plan, including technique, device sizing, and operative working projections. RESULTS: The study included eight patients with their respective EVIAS 3D aneurysm models. Pre-operative simulation was performed for the first in-human robotic-assisted neurovascular interventions (n=2) and new generation flow-diverter stents (n=6). Aneurysms were located in both the anterior (n=5) and posterior (n=3) circulation and were on average 11.0±6.5 mm in size. We found reliable reproduction of the aneurysm features and similar dimensions of the parent vessel anatomy between the 3D models and patient anatomy. Information learned from pre-surgical in vitro simulation are described in detail, including an improved patient treatment plan, which contributed to successful first in-world procedures with no intraprocedural complications. CONCLUSIONS: Pre-procedural rehearsal using patient-specific 3D models provides precise procedure planning, which can potentially lead to greater operator confidence, decreased radiation dose and improvements in patient safety, particularly in first in-human experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".