Deep Learning in Emergency Radiology: Evaluating a Tool for Automated ASPECT Scoring in Acute Ischemic Stroke
Bibliographic record
Abstract
ABSTRACT Background and Purpose The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) quantifies ischemic damage in middle cerebral artery territory strokes, guiding treatment decisions including thrombolysis and mechanical thrombectomy. However, inter‐rater variability in scoring limits its clinical reliability. Automated ASPECTS tools like CINA‐ASPECTS (v1.4.3, Avicenna.AI) have shown potential to enhance consistency and efficiency. This study validated CINA‐ASPECTS using a multinational, multi‐scanner external dataset against a ground truth (GT) established by expert neuroradiologists. Methods Head NCCT scans from 339 patients (20 ASPECTS regions each) were collected from clinical sites in the United States, France, and Japan. Five US board‐certified expert neuroradiologists participated in establishing the GT using NCCT and additional imaging when available, blinded to each other and the device output. CINA‐ASPECTS outputs were compared to this GT on a region‐based and score‐based level. Stratified analyses were conducted by scanner manufacturer, cortical versus deep regions, and individual ASPECTS regions. Results CINA‐ASPECTS demonstrated 72.8% sensitivity, 91.8% specificity, and an area under the receiver operating characteristic curve of 0.823 in region‐based analysis. Score‐based agreement showed an intraclass correlation coefficient (ICC) of 0.83 [95% CI: 0.79–0.86]. Dichotomized score‐based analysis showed good specificity, sensitivity, and ICC. Stratified analysis confirmed consistent performance across scanner manufacturers and cortical/deep ASPECTS regions. Performance was robust regardless of ASPECTS region, potentially aiding in the scoring of difficult regions. Conclusions CINA‐ASPECTS reliably identifies ischemic changes across diverse clinical and imaging conditions, achieving good agreement with expert consensus. These findings support its utility in clinical decision‐making for acute stroke management.
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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 | 0.006 | 0.026 |
| 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 | 0.001 |
| 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".