Automated ischemic stroke prediction from Alberta stroke Program Early CT scores utilizing optimized progressive cyclical convolutional neural network on CT scans
Bibliographic record
Abstract
Alberta Stroke Program Early CT Score (ASPECTS) is a systematic approach to evaluating ischemic changes on non-contrast CT (NCCT) scans of acute ischemic stroke (AIS) patients, but it requires expert interpretation and often yields inconsistent results. This study proposes an Automated Ischemic Stroke Prediction model from ASPECTS utilizing an Optimized Progressive Cyclical Convolutional Neural Network (PCCNN) enhanced with the Bitwise Arithmetic Optimization Algorithm (BAOA). The framework employs Dual Image-Adaptive Learnable Filter (DIALF) for skull stripping and normalization and Localized Sparse Incomplete Multi-View Clustering (LSIMVC) for accurate ASPECTS-region segmentation. The model was trained and validated using dual clinical datasets from Huaxi Hospital and Hangzhou First People’s Hospital. Experimental results show that the proposed method achieves 99.22% accuracy, 98.30% precision, 98.02% sensitivity, and an AUC of 0.92, outperforming existing ASPECTS-based models such as DGA3-Net and DL-ASPECTS-AIS by over 25–30% in most metrics, while reducing computational time by up to 31.9%. These findings confirm that the proposed ASPECTS-ISP-PCCNN-CTS framework delivers superior diagnostic accuracy, efficiency, and generalizability for early ischemic stroke assessment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".