Abstract 249: In‐silico Versus In‐Vitro Evaluation of a New Stent‐Retriever Design: A Novel Approach to Pre‐clinical Development
Bibliographic record
Abstract
Introduction In‐vitro evaluation (analysis in a bench top flow model) is the gold standard and a regulatory requirement for pre‐clinical investigation of devices being developed for endovascular thrombectomy (EVT). In‐silico evaluation (computer simulated analysis in a virtual stroke model) has the potential to test and optimize large number of stent‐retriever design variations in a relatively time and cost‐effective manner. Further, new design concepts can be tested across multiple anatomical scenarios. We aim to validate the utility of in‐silico evaluation when compared with in‐vitro evaluation for the SuperNova Stent‐retriever. Methods In‐silico analysis was performed using a virtual thrombectomy model, built using data from a fine‐grained finite‐element model, to estimate the probability of successful recanalization and emboli in new/distal territory. Neurovascular anatomy of the virtual model was built based on digital subtraction angiography of an in‐vitro model (Sim Agility, Mentice, Inc). Gravity Medical Technology’s SuperNova Stent‐retriever was the device under investigation‐ physical device for in‐vitro analysis and virtual replica for in‐silico analysis (built using finite element analysis incorporating various physical metrics of the stent‐retriever). Experiments performed for in‐vitro analysis were replicated for in‐silico analysis. Multiple thrombectomy scenarios were defined and validation analysis was performed. Data was analyzed using SPSS 23 (IBM, Armonk, NY) Results We defined multiple thrombectomy scenarios including 1 cm red blood clot in M1 artery, 2 cm bifurcation M1‐M2 red blood clot (Y‐shaped clot), 1 cm white blood clot in M1 artery, and 1 cm white blood clot in superior M2 artery. The figure below demonstrates a pictorial representation of the first scenario‐ 1 cm red blood clot in M1 artery followed by mechanical thrombectomy simulation using the SuperNova stent‐retriever. Per our in‐vitro analysis (10 thrombectomy experiments), the rate of first pass effect and the rate of complete recanalization after a maximum of 3 passes using SuperNova stent‐retriever was 50% and 90%, respectively. Complete data from in‐silico analysis is being generated and will be presented at the conference. Preliminary data suggests high degree of concordance. Conclusion In‐silico analysis has the potential to improve and expedite pre‐clinical thrombectomy device development. Further studies are required to better understand the scope and potential of this technology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".