Development of an in-vitro model based on patient vessel geometry for simulated use testing in neurointerventional surgery
Bibliographic record
Abstract
BackgroundNeurointerventionalists use in-vitro vascular models to train for worst-case scenarios and test new devices in a simulated use environment to predict clinical performance. According to the Food and Drug Administration (FDA), any neurovascular navigation device should be able to successfully navigate two 360-degree turns and two 180-degree turns at the distal portion of the anatomical model. Here, we present a device benchmarking vascular model that complies with FDA recommendations.MethodsOur vascular model was assembled from quantitative characterization of 49 patients who underwent CT angiography either for acute ischemic stroke caused by large vessel occlusion or for aneurysm treatment. Following complete characterization of these data, the vascular segments were 3D reconstructed from CT angiograms of 6 selected patients that presented with challenging anatomy. The curvature and total rotational angle were calculated for each segment and the anatomical parts that complied with FDA recommendations were fused together into a single in-vitro model.ResultsThe model was constructed containing two common carotid branches arising from a type two aortic arch and the dimensions of the overall model exceeded the recommendations of the FDA. Two experienced neurointerventionalists tested the model for navigation difficulty using several devices on an in-vitro perfusion system and concluded that the model provided a realistic, challenging scenario.ConclusionsThis model provides a first prototype designed according to FDA recommendations of cumulative angle while also integrating an aggregation of actual patient-specific anatomy. The availability of this clinically relevant benchmark model presents a potential standardized approach for neurovascular device testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".