Machine learning enhanced noninvasive transcranial stroke detection using a portable eddy current damping sensor
Bibliographic record
Abstract
Stroke remains a leading cause of morbidity and mortality globally, with timely diagnosis critical for effective treatment. Current imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), often face limitations in availability and timeliness, leading to diagnostic delays that worsen patient outcomes. This study presents the development and validation of a portable, noninvasive eddy current damping (ECD) sensor for rapid stroke detection. Building on previous research and optimized through benchtop and phantom studies, this device incorporates advanced coil designs and algorithms to distinguish between hemorrhagic stroke patients and healthy individuals by detecting electrical conductivity variations between normal brain tissue and accumulated blood. Results demonstrate high degrees of accuracy and specificity, promising to enable real-time bedside differentiation of stroke patients. This ECD sensor may expand access to timely stroke care across prehospital, clinical, and remote settings and improve patient outcomes by expediting targeted treatment and rapid triage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".