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 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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".