Utility of automated CT perfusion software in acute ischemic stroke with large and medium vessel occlusion
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of large vessel occlusion (LVO) in acute stroke often requires CT angiography (CTA). Automated CT perfusion (CTP) software, which identifies blood flow abnormalities, enhances LVO diagnosis and patient selection for endovascular thrombectomy (EVT). This study evaluates the sensitivity of automated CTP images in detecting perfusion abnormalities in patients with acute ischemic stroke (AIS) and LVO or medium vessel occlusion (MeVO), compared to CTA. METHODS: We screened acute ischemic stroke patients presenting within 24 h who underwent CT, CTA, and CTP as per institutional protocol. RAPID AI software processed CTP images, while neuroradiologists reviewed CTA for intracranial arterial occlusions. Sensitivity, specificity, and accuracy of automated CTP maps in detecting occlusions were assessed. RESULTS: Of 790 screened patients, 31 were excluded due to lack of RAPID CTP data or poor-quality scans, leaving 759 for analysis. The median age was 71 years (IQR: 61-81), with 47% female. Among them, 678 had AIS, and 81 had AIS ruled out. CTA identified arterial occlusion in 562 patients (74%), with corresponding CTP abnormalities in 537 patients (Tmax > 6 sec). In the 197 without occlusion, CTP was negative in 161. Automated CTP maps had a sensitivity of 95.55% (CI 95: 93.50-97.10%), specificity of 81.73% (CI 95: 75.61-86.86%), negative predictive value of 98.22% (CI 95: 97.39-98.79%), positive predictive value of 63.54% (CI 95: 56.46-70.09%), and overall accuracy of 85.18% (CI 95: 82.45-87.64%). CONCLUSIONS: Automated CTP maps demonstrated high sensitivity and negative predictive value for LVOs and MeVOs, suggesting their usefulness as a rapid diagnostic tool, especially in settings without expert neuroradiologists.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".