Abstract WMP13: Latest Generation of Flat Detector CT as a Periinterventional Diagnostic Tool: A Comparative Study with Multidetector CT
Bibliographic record
Abstract
Background and Purpose: Flat detector CT has been used as a periinterventional diagnostic tool in numerous studies with mixed results regarding image quality and detection of intracranial lesions. We compared the diagnostic aspects of the latest generation flat detector CT to a standard multidetector CT. Materials and Methods: One hundred and two patients were included in our retrospective study. All patients had undergone interventional procedures; flat detector CT was acquired periinterventionally and compared to a postinterventional multidetector CT regarding the depiction of ventricular/subarachnoidal spaces, the detection of intracranial hemorrhage and the delineation of ischemic lesions by using an ordinal scale. Ischemic lesions were quantified with the Alberta Stroke Program Early CT score on both exams. Two neuroradiologists of various experience grades and a medical student scored the anonymized images, blinded to clinical history. Results: The two methods were diagnostic equal in evaluating the ventricular system and the subarachnoidal spaces. Subarachnoidal, intraventicular and intraparenchymal hemorrhages were detected with a sensitivity of 95%, 94%, 100% and specificity of 97%, 97% and 99% respectively using flat detector CT. Grey-white differentiation was feasible in the majority of flat detector CT scans and ischemic lesions were detected with a sensitivity of 71% on flat detector CT, compared to multidetector CT scans. Alberta Stroke Program Early CT score values correlated highly with a correlation coefficient of r=0,78. Conclusion: The latest generation of flat detector CT is a reliable tool for the detection of intracranial hemorrhage and extended ischemic lesions. Flat detector CT acquired with angiography systems could be increasingly used in acute stroke diagnostics (so called one stop imaging) with a massive impact in door to groin times.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".