Abstract 57: Deployment of Portable, Bedside, Low-Field Magnetic Resonance Imaging for Evaluation of Stroke Patients
Bibliographic record
Abstract
Background: Magnetic resonance imaging (MRI) is a powerful modality for diagnosing stroke. Conventionally, patients must travel to the location of a high-field MRI device. Advances in low-field MRI have enabled acquisition of clinically useful images using a portable device at the bedside. The feasibility of using point of care (POC) MRI in a clinical stroke setting is unknown. Objective: To determine the safety and feasibility of portable, bedside, low-field MRI in a clinical setting. Design/Methods: POC MRI exams were performed in Yale’s Neuroscience Intensive Care Unit (NICU) from July 2018 to August 2019. Images were acquired at the bedside using a standard 110V, 15A power outlet. The environment included the bedside vitals monitor, ventilators and intravenous infusion pumps. Exams were performed by research staff trained to operate the POC scanner in the absence of a trained MRI technician. No special precautions were necessary to remove ferrous metals from the room. Scan parameters were controlled using a tablet computer interface, and images were available immediately after acquisition. Results: POC MRI was obtained in 85 stroke cases (46% female, ages 18-96 years, 46% ischemic stroke, 34% intracerebral hemorrhage, 20% subarachnoid hemorrhage). Scans were obtained within 7 days of symptom onset. NIHSS scores ranged from 1 to 29 (median of 7). Of the 85 patients analyzed, 68 underwent T2-weighted imaging, 72 underwent FLAIR imaging, and 39 underwent diffusion weighted imaging (DWI). DWI was only tested in ischemic stroke cases. Patients’ BMI ranged from 20.0 to 46.5 with a median of 26.7. The majority 74 (87%) of patients completed the entire exam. Five patients (6%) were unable to fit in the scanner’s 30 cm opening, while 6 patients (7%) experienced claustrophobia resulting in early termination of the exam. Mean exam time was 28.9 ± 8.4 minutes. The 64 mT static magnetic field, gradient and RF pulses of the POC MRI scanner did not interfere with NICU equipment, and no significant adverse events occurred. Conclusions: We report the first use of a portable, low-field MRI system to image stroke patients at the bedside. This early work suggests our approach is safe and viable in a complex clinical care environment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".