Abstract WP222: External Validation of an Automated Hemorrhage Detection and Segmentation Algorithm on Follow-up CT scans in the AcT trial
Bibliographic record
Abstract
Background and Aims: Machine learning models have shown promising potential for automated hemorrhage detection and segmentation, alleviating highly time-consuming manual contouring and facilitating rapid clinical diagnosis. External validation is essential to assess model generalizability and performance across new dataset configurations. To this end, we externally validated a novel model for hemorrhage detection and segmentation in an unseen randomized-controlled trial dataset. Methods: A novel segmentation architecture based on denoising diffusion probabilistic models was utilized for segmenting hemorrhage. The model had been trained upon a set of 331 CT scans with manually segmented parenchymal hemorrhage lesions. External validation was conducted using the AcT (Alteplase compared to Tenecteplase) trial in which patients underwent post-thrombolysis follow-up CT scans. Ground truth regarding hemorrhage presence was determined by expert readers blinded to the algorithm’s results who performed manual contouring and graded hemorrhages using the Heidelberg classification. Model performance was then evaluated through using diagnostic performance measures and the Dice coefficient. Results: Among the 1338 patients with follow-up CT scans, two types of hemorrhages were adjudicated: (a) any kind of hemorrhage (230/1338) and (b) remote or local hemorrhages classified as PH1 or worse (170/1338). The algorithm achieved sensitivity of 89.4% (95% CI 84.8-94.1%) for hemorrhages ≥PH1 and 61.7% (95% CI 55.5-68.0%) for any kind of hemorrhage where specificity of 92.8% (95% CI 91.3-94.3%), positive predictive value of 64.0% (95% CI 57.6-70.3%), negative predictive value of 92.1% (95% CI 90.5-93.7%) and accuracy of 87.4% was achieved. Dice was 0.582 (95% CI 0.538, 0.629) for any kind of hemorrhage and 0.611 (0.555, 0.667) for hemorrhages ≥PH1. Conclusions: Our automated model for hemorrhage segmentation demonstrated robust performance in this external clinical trial dataset, achieving high sensitivity for large hemorrhages and high specificity for smaller hemorrhages. Future work will seek to further optimize the algorithm’s performance for detection and segmentation of smaller hemorrhages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".