Evaluation of an artificial intelligence-based software device for detection of intracranial haemorrhage in teleradiology practice
Bibliographic record
Abstract
Abstract Objectives Artificial Intelligence (AI) algorithms have the potential to assist radiologists in the reporting of head CT scans. We investigated the performance of an AI-based software device used in a large teleradiology practice for intracranial haemorrhage (ICH) detection. Methods A randomly selected subset of all noncontrast CT head (NCCTH) scans from patients aged ≥ 18 years referred for urgent teleradiology reporting from 44 different hospitals within the UK over a 4-month period was considered for this evaluation. 30 auditing radiologists evaluated the NCCTH scans and the AI output retrospectively. Agreement between AI and auditing radiologists is reported along with failure analysis. Results A total of 1315 NCCTH scans from as many distinct patients were evaluated. 112 (8.5%) scans had ICH. Overall agreement, positive percent agreement, negative percent agreement, and Gwet’s AC1 of AI with radiologists were found to be 93.5% (95% CI: 92.1–94.8), 85.7% (77.8–91.6), 94.3% (92.8–95.5) and 0.92 (0.90–0.94) respectively in detecting ICH. 9 out of 16 false negative outcomes were due to missed subarachnoid haemorrhages and these were predominantly subtle haemorrhages. The most common reason for false positive results was due to motion artefacts. Conclusions AI demonstrated very good agreement with the radiologists in the detection of ICH.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".