Automated ischemic stroke prediction from Alberta stroke Program Early CT scores utilizing optimized progressive cyclical convolutional neural network on CT scans
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it