Abstract WMP72: Accurate LVO and MeVO Detection Using a Multiphase CT Angiography Perfusion-Based Tool
Bibliographic record
Abstract
Background and Aims: Early and accurate detection of vessel occlusion is essential for the management of acute ischemic stroke (AIS). Current automated occlusion detection tools mostly rely on single-phase CTA (sCTA), which makes it challenging to accurately identify Medium Vessel Occlusions (MeVO). Multiphase CTA (mCTA), which consist of an additional two low-dose scans to the traditional CTA workflow, may be able to allow precise detection of MeVO occlusions. In this work, we aim to validate StrokeSENS mCTA Occlusion Detection in its ability to detect LVOs and MeVOs up to and including the M3/A3/P3 segments of the Middle/Anterior/Posterior Cerebral Arteries (MCA/ACA/PCA). Methods: Validation was performed on 512 studies of suspected AIS patients with baseline mCTA. Studies had MeVO (PCA:11, ACA:4, MCA:83), LVO (ICA:116, PCA:8, MCA:263), and no occlusion: 27. Expert neuroradiologists annotated the presence and location of the occlusion. StrokeSENS mCTA Occlusion Detection processed mCTA data to produce hypoperfusion extent probability maps, and presence or absence of occlusion. To validate the ability of the tool to detect occlusion, we used ROC analysis, specificaly the AUC and Sensitivity/Specificity metrics. Results: The full cohort AUC was 96.7% (95% C.I.: [94.7%,97.7%]). For LVO subgroups, ICA AUC=98.2%, MCA AUC=98.0%, PCA AUC=68.5%. For MeVO, MCA AUC=95.0%, ACA AUC=78%, PCA AUC=91.2%. At the selected operational point, overall Sensitivity was 86.0% (95% C.I.: [0.829, 0.891], N=485), and Specificity was 92.6% (95% C.I.: [0.766, 0.979], N=27). Figure 1 shows examples of the hypoperfusion extent produced by the tool for LVO and MeVO cases. Conclusion: In this work, we evaluated the tool StrokeSENS Occlusion Detection in its ability to detect LVO and MeVO occlusions. The tool has shown very high accuracy in detection occlusions up to and including the M3/A3/P3 segments of the Middle/Anterior/Posterior Cerebral Arteries (MCA/ACA/PCA).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".