Abstract WP413: Portable, Bedside, Point of Care Magnetic Resonance Imaging in an Intensive Care Setting for Intracranial Hemorrhage
Bibliographic record
Abstract
Background: Radiographic diagnosis of intracranial hemorrhage (ICH) is a critical determinant of stroke care pathways requiring patient transport to a neuroimaging suite. Advances in low-field MRI have made it possible to obtain clinically useful imaging at the point of care (POC). Aim: The aim of this study was to obtain preliminary data regarding the ability of a bedside POC MRI scanner to detect ICH. Methods: We studied 36 patients with a diagnosis of ICH (n=18) or ischemic stroke (n=18). Five blinded readers independently evaluated T2W and FLAIR exams acquired prospectively on a 64 mT, portable bedside MRI system (Hyperfine Research, Inc). Kappa coefficients (κ) were calculated to determine inter-rater agreement. Ground truth was obtained from the clinical report of the closest conventional imaging study (17.9 ± 10.4 hours) and verified by a core reader. For each exam, majority consensus among raters was used to determine sensitivity. Results: ICH volume ranged from 4 to 101 cc (median of 13 cc). Exams were acquired within 7 days of symptom onset (51.1 ± 28.8 hours). A pathologic lesion was identified on every exam with 100% sensitivity. Sensitivity for distinguishing any hemorrhage was 89% and specificity was 83%. The mean sensitivity and specificity for individual raters was 79% and 69%, respectively. When limited to supratentorial hemorrhage, consensus sensitivity was 94%. For ICH cases detected by all raters (n=9), there was 100% accuracy for localizing the bleed (lobar vs. non-lobar) with perfect agreement among raters (κ = 1, p <0.0001). There was substantial agreement for identifying intraventricular hemorrhage (IVH) (κ = 0.72, p < 0.0001). Sensitivity for IVH was 100% based on rater consensus. Figure 1 shows a POC exam with an ICH and IVH. Conclusions: These data suggest that low-field, POC MRI may be used to detect hemorrhagic stroke at the bedside. Further work is needed to evaluate this approach in the hyperacute setting and across a wide range of ICH characteristics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".