Dynamic perfusion analysis in acute ischemic stroke: A comparative study of two different softwares
Bibliographic record
Abstract
BACKGROUND: In clinical practice, decisions often must be made rapidly; therefore, automated software is useful for diagnostic support. Perfusion computed tomography and follow-up evaluation of perfusion data are valuable tools for selecting the optimal recanalization therapy in patients with acute ischemic stroke. OBJECTIVE: This study aimed to compare commercially available software used to evaluate stroke patients prior to thrombectomy. METHODS: The performance of Olea Sphere (OlS) software vs. CT Neuro Perfusion from Syngo (Sy), as well as the electronic Alberta Stroke Program Early Computed Tomography Score (e-ASPECTS) software vs. an experienced radiologist, were compared using descriptive statistics including significance analysis, Spearman's correlation, and the Bland-Altman agreement analysis. For this purpose, 43 data sets of patients with stroke symptoms related to the middle cerebral artery territory were retrospectively post-processed with both tools and analyzed. RESULTS: The automatic e-ASPECTS showed high agreement with an expert rater assessment of the ASPECTS. Using OlS and Sy, we compared the parameters for the ischemic core (relative cerebral blood flow), Time to maximum (Tmax) for the penumbra, and the relative mismatch between these two values. Overall, both software tools achieved good agreement, and their respective values correlated well with each other. However, OlS predicted significantly smaller infarct core volumes compared with Sy. CONCLUSIONS: Although the absolute values have a certain degree of variation, both software programs have good agreement with each other.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".