Evaluation of the novel medical imaging software e-ASPECTS for patient selection in stroke
Bibliographic record
Abstract
Introduction: The interpretation of a CT scan of acute ischaemic stroke patients in the acute setting requires experience and carries significant variability. The Alberta Stroke Program Early CT score (ASPECTS) is an established 10-point quantitative topographic CT scan score to assess early ischaemic changes on plain CTs of acute stroke patients. We compared the performance of the standardised and fully automated software, e-ASPECTS (Brainomix, www.brainomix.com) with 3 expert neuroradiologists. Method: The baseline non-contrast enhanced CT scans of 132 acute stroke patients were evaluated by 3 expert neuroradiologists and e-ASPECTS using the ASPECTS method. Ground truth was determined by an independent core lab with access to follow-up CT/MR scans. e-ASPECTS is a standardised, fully automated ASPECTS scoring software tool that carries out a 3D registration/segmentation of ASPECTS regions and uses machine learning techniques for scoring. The sensitivity and specificity of e-ASPECTS to the 3 experts was compared by noninferiority analysis.Results: On average, e-ASPECTS deviated from the ground truth by less than one point (+0.8). For the 3 experts the deviation from the ground truth was +1.2, −0.7 and +1.1, respectively. Receiver operating characteristic (ROC) analysis on per region and per score basis showed that e-ASPECTS sensitivity and specificity is equivalent to the 3 experts. Discussion: e-ASPECTS is statistically non-inferior and equivalent to expert neuroradiologists. e-ASPECTS is a valuable tool to assist patient selection for both intravenous and endovascular stroke treatment.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".