Evaluation of the novel medical imaging software e-ASPECTS for patient selection in stroke
Bibliographic record
Abstract
<i>Introduction:</i> 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. <i>Method:</i> 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.<i>Results:</i> On average, e-ASPECTS deviated from the ground truth by less<br/>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. <i>Discussion:</i> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".