Latest generation of flat detector CT as a peri-interventional diagnostic tool: a comparative study with multidetector CT
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Flat detector CT (FDCT) has been used as a peri-interventional 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 FDCT with standard multidetector CT (MDCT). MATERIALS AND METHODS: 102 patients were included in our retrospective study. All patients had undergone interventional procedures. FDCT was acquired peri-interventionally and compared with postinterventional MDCT regarding depiction of ventricular/subarachnoidal spaces, detection of intracranial hemorrhage, and delineation of ischemic lesions using an ordinal scale. Ischemic lesions were quantified with the Alberta Stroke Program Early CT Scale (ASPECTS) on both examinations. Two neuroradiologists with varying grades of experience and a medical student scored the anonymized images separately, blinded to the clinical history. RESULTS: The two methods were of equal diagnostic value regarding evaluation of the ventricular system and the subarachnoidal spaces. Subarachnoidal, intraventricular, and parenchymal hemorrhages were detected with a sensitivity of 95%, 97%, and 100% and specificity of 97%, 100%, and 99%, respectively, using FDCT. Gray-white differentiation was feasible in the majority of FDCT scans, and ischemic lesions were detected with a sensitivity of 71% on FDCT, compared with MDCT scans. The mean difference in ASPECTS values on FDCT and MDCT was 0.5 points (95% CI 0.12 to 0.88). CONCLUSIONS: The latest generation of FDCT is a reliable and accurate tool for the detection of intracranial hemorrhage. Gray-white differentiation is feasible in the supratentorial region.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".