Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients
Bibliographic record
Abstract
BACKGROUND: The Alberta Stroke Program Early CT Score (ASPECTS) is a systematic method of assessing the extent of early ischemic change on non-contrast computed tomography in patients with acute ischemic stroke. Our objective was to validate an automated ASPECTS scoring method we recently developed on a large data set. MATERIALS AND METHODS: We retrospectively collected 602 acute ischemic stroke patients' non-contrast computed tomography scans. Expert ASPECTS readings on non-contrast computed tomography were compared to automated ASPECTS. Statistical analyses on the total ASPECTS, region level ASPECTS, and dichotomized ASPECTS (≤4 vs. >4) score were conducted. RESULTS: In total, 602 scans were evaluated and 6020 (602 × 10) ASPECTS regions were scored. Median time from stroke onset to computed tomography was 114 min (interquartile range: 73-183 min). Total ASPECTS for the 602 patients generated by the automated method agreed well with expert readings (intraclass correlation coefficient): 0.65 (95% confidence interval (CI): 0.60-0.69). Region level analysis showed that the automated method yielded accuracy of 81.25%, sensitivity of 61.13% (95% CI: 58.4%-63.8%), specificity of 86.56% (95% CI: 85.6%-87.5%), and area under curve of 0.74 (95% CI: 0.73-0.75). For dichotomized ASPECTS (≤4 vs. >4), the automated method demonstrated sensitivity 97.21% (95% CI: 95.4%-98.4%), specificity 57.81% (95% CI: 44.8%-70.1%), accuracy 93.02%, and area under the curve of 0.78 (95% CI: 0.74-0.81). For each individual region (M1-6, lentiform, insula, and caudate), the automated method demonstrated acceptable performance. CONCLUSION: The automated system we developed approached the stroke expert in performance when scoring ASPECTS on non-contrast computed tomography scans of acute ischemic stroke patients.
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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 | 0.009 | 0.025 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".