INFLUENCE OF RECONSTRUCTION KERNEL AND SLICE THICKNESS ON AUTOMATED ASPECTS PERFORMANCE FOR DETECTION OF EARLY ISCHEMIC CHANGES ON NON-CONTRAST BRAIN COMPUTED TOMOGRAPHY SCANS
Bibliographic record
Abstract
Background and Aims:With the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), 10 brain-regions are dichotomously scored on presence of ischemic stroke damage. Considerable variability in CT-scanner parameter settings is seen in clinical practice. Optimized parameters could improve the performance of ASPECTS software. We evaluated the influence of CT-scan parameter settings on computed ASPECTS (c-ASPECTS 2.0.1, Frontier, Siemens Healthineers, Forchheim, Germany).Methods:Prospectively, patients with acute stroke symptoms received a Non Contrast CT-scan (Siemens Somatom Definition Edge). Thirty consecutive patients with middle cerebral artery (MCA) occlusion were included. c-ASPECTS were assessed in images with different Siemens CT reconstruction kernels (J30s/J37s/J40s and H20s/H30s/H31s) and slice thicknesses (2.0-5.0 mm). Ground truth ASPECTS was provided by an expert with unrestricted data access. Scans (J40s: 5.0 mm and J30s: 2.0 mm) were evaluated by four readers for ASPECTS. For every combination of parameters, we calculated the agreement of ground truth with c-ASPECTS and c-ASPECTS regions, respectively. Agreement of c-ASPECTS across all parameter combinations was assessed. Correlation of ground truth with readers and c-ASPECTS was calculated.Results:Comparison of ground truth with c-ASPECTS and c-ASPECTS regions across all parameter combinations shows ICCu2019s of 0.421-0.609 and agreement of 0.80-0.82, respectively. No significant differences were found between images reconstructed with different kernels or slice thicknesses. Agreement of c-ASPECTS across all parameter combinations shows an ICC of 0.936. Comparison of ground truth with readers and c-ASPECTS resulted in comparable correlations (ICCu2019s of 0.541-0.811 and 0.519, respectively).Conclusions:Reconstruction kernels and slice thicknesses do not significantly affect the performance of c-ASPECTS.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.024 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".