Abstract TMP59: Automated Analysis of Dynamic Computed Tomographic Angiography Perfusion Maps Accurately Identifies Regional Hypoperfusion in Minor Stroke Patients
Bibliographic record
Abstract
Background: Multi-phase computed tomographic angiography (mCTA) perfusion maps can help confirm the diagnosis of stroke which can be especially challenging when the symptoms are minor. The current study proposes an automated stroke detection method for identifying patient stroke status based on volumetric analysis of mCTA perfusion maps applied to minor stroke patient populations. Methods: Minor stroke patients were acutely imaged with mCTA and CTP. mCTA perfusion maps were created using an extreme gradient boosting trees classifier with a CTP TMAX>6s ground truth. Volumes for each patient for each hemisphere were calculated from ten perfusion thresholds equally spaced from the minimum and maximum of the scaled perfusion map. The ten volumes form the variables in a logistic regression algorithm trained on the known stroke status of the hemisphere. 10-fold cross validation was implemented in addition to receiver-operating characteristic (ROC) analysis to produce accuracy, sensitivity, specificity, and area-under-curve (AUC). Results: In total 82 minor stroke patients (median age: 71, 48% female, median NIHSS: 3). 78% had identifiable intracranial occlusions. The analysis generated an ROC curve with an AUC of 81%. Cross-validation produced accuracy, specificity, and sensitivity of 76%, 87%, and 65%, respectively. Conclusion: The stroke detection model developed in the current study accurately categorized stroke and healthy hemispheres in minor stroke. Based on these results dynamic CTA perfusion could help diagnose stroke when the symptoms are mild thus providing accessible and accurate stroke diagnosis in primary stroke centres.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".