Abstract TP187: Automated Assessment of Intracerebral Hemorrhage Volumes using the VIOLA tool Performs Similarly to ABC/2 in Predictive Modelling
Bibliographic record
Abstract
Introduction: Hemorrhage volume is a key prognostic characteristic for intracerebral hemorrhage (ICH) patients and has been included as an aspect of critical assessments, such as the ICH score. Hemorrhage measurement can be time consuming, prompting the value of automated hemorrhage assessment tools. Many of these, however, remain unvalidated in predictive models. We compared the performance of the VIOLA deep learning model (developed at www.crai.no) against the conventional ABC/2 method. Methods: An ICH group was created by randomly sampling from the Registry for Neurological Endpoint Assessment among patients with Ischemic and Hemorrhagic Stroke (REINAH) database, which that includes completed hemorrhage characteristic assessments (location, laterality, volume calculated via ABC/2, and intraventricular hemorrhage (IVH) presence). Computed tomography (CT) images used for ABC/2 calculations were retrieved, and the previous trained VIOLA deep learning tool was deployed locally to calculate the volume of parenchymal and intraventricular hemorrhage. ICH volumes were compared using the Wilcoxon Signed Rank test, and separate multivariable logistic regression models to predict in-hospital mortality were fit, including patient demographics, comorbidities, clinical characteristics, and volume quartiles, and adjusted odds ratios are reported. Akaike information criteria (AIC) was used to compare separate ABC/2 and VIOLA models. Results: A total of 407 patients were retrieved from RIENAH. Included patients had a median age of 68 [55-77], were 42.8% female, 43.7% White, 18.7% Black, 22.4% Hispanic, 9.6% Asian, and 5.7% Other/Declined. IVH was present in 48.4% of the cohort and 24.6% experienced in-hospital mortality. Hemorrhages assessed via VIOLA yielded a median of 23.70 [7.02-69.18] cm 3 , significantly higher than those determined by ABC/2 (14.33 [5.19-37.47] cm 3 ,p<0.001), and more patients showed volumes over 30cm 3 using VIOLA (78 (43.7%) vs 131 (32.2%)). In logistic regression modelling, patients with top-quartile hemorrhage volumes in either ABC/2 or VIOLA assessment had higher odds of in-hospital mortality (ABC/2 aOR: 3.32 [1.01-10.94]; VIOLA: 4.50 [1.33-15.19]). The VIOLA-based model showed similar AIC to the ABC/2 model, indicating roughly equivalent model fit (263.9 vs 261.9) Discussion: Hemorrhage volume assessment using the VIOLA tool performed comparably than ABC/2 in prognostic modelling and provides a viable alternative to manual assessment.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".