Automated evaluation of ASPECTS from brain computerized tomography of patients with acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Precise evaluation of brain computerized tomography (CT) is a crucial step in acute ischemic stroke evaluation. Electronic Alberta Stroke Program Early CT Score (E-ASPECTS) helps in the selection of patients who may be eligible for thrombolysis. This paper seeks to assess the performance of emergency physicians (EPs) in the evaluation of ASPECTS scores with and without the use of E-ASPECTS and to compare their results with neuroradiologists. METHODS: A total of 116 patients were selected. Initially, two EPs and two neuroradiologists evaluated the admission nonenhanced CT without E-ASPECTS. Then, after 30 days, they re-evaluated the images using E-ASPECTS. Sensitivity, specificity, Matthew's correlation coefficients (MCC), and receiver operating characteristic curves were generated for analysis before and after the software use. RESULTS: Eps' performances improved when they used E-ASPECTS, with their results closer to those obtained by neuroradiologists. In the initial evaluation, MCC values for the two EPs were -0.01 and 0.04, respectively. After the software assistance, they obtained 0.38 and 0.43, respectively, which was closer to the scores obtained by the neuroradiologists (0.53 and 0.39, respectively). DISCUSSION: This is the first study that has specifically compared neuroradiologists' and EPs' performances before and after using E-ASPECTS. E-ASPECTS assisted and improved the evaluation of the images of patients with acute ischemic stroke. CONCLUSION: Artificial intelligence in the emergency room may increase the number of patients treated with tissue-type plasminogen activators.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".