VALIDATION OF AUTOMATED ASPECTS SOFTWARE FOR DETECTION OF EARLY ISCHEMIC BRAIN CHANGES ON NON-CONTRAST CT SCANS
Bibliographic record
Abstract
Background and Purpose: In the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), 10 brain regions are dichotomously scored on presence of ischemic stroke damage. However, considerable inter- and intra-reader variability exists, even for expert readers. We evaluated computed ASPECTS (ASPECTS 2.0.1, Frontier, Siemens Healthineers, Forchheim, Germany) in comparison to expert readers.Methods: Each included baseline non-contrast CT-scan (5 mm slice thickness) from the MR CLEAN trial (n=463) was evaluated by four expert readers for manual ASPECTS.Two observers provided a two-observer consensus for ASPECTS-regions (normal/abnormal) as ground truth for training and testing (0.2/0.8 division). The remaining two observers where used to provide individual ASPECTS-region scores.A Frontier ASPECTS region score specificity of u226590% was used to determine the software threshold (relative density difference between affected and contralateral region). Sensitivity, specificity and receiver-operating characteristic curves were calculated. Thereafter, we calculated ICC[1,1], agreement per region and trichotomized ASPECTS (0-4, 5-7, 8-10) for Frontier ASPECTS and expert readers, and between the expert readers in the test set.Results: A subset (n=459/463) was included. In the training set (n=104), a threshold of 4.7-5.6% was found for a specificity of u226590%, resulting in a sensitivity and area under the curve of 33-49% and 0.741-0.785. In the test set (n=355) the corresponding results were 89-89%, 41-57% and 0.750-0.795, respectively. Comparison of ground truth with other observers resulted in an ICC of 0.383-0.464, ASPECTS region agreement of 0.77-0.81 and trichotomized ASPECTS agreement of 0.57-0.60 Comparison of ground truth with Frontier ASPECTS resulted in an ICC of 0.537, ASPECTS region agreement of 0.78 and trichotomized ASPECTS agreement of 0.60.Conclusions: The performance of Frontier ASPECTS is comparable to expert readers.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".