Rapid Competency in NIR-Based Neuroimaging: Training Non-Experts for Head Trauma Triage
Bibliographic record
Abstract
Abstract Objective To evaluate whether novice, non-specialist operators can rapidly learn to use a handheld near-infrared (NIR) head scanner and maintain scan quality after brief training, supporting its use for point-of-care triage when computed tomography is unavailable or delayed. Methods Thirty-two right-handed adults with no prior NIR experience received a brief standardized training session (∼2 minutes) on the ArcheOptix NIRD® device, which detects intracranial hemorrhage by tracking hemoglobin absorption during guided scalp scans. Operators then completed two full-head scans on a healthy volunteer: an initial competency assessment (Scan 1) and a follow-up assessment after one day without refresher training (Scan 2). Performance metrics included total scan time, frequency of repeat scans prompted by loss of contact or light leaks, and mean scanpath time as an index of handling efficiency and consistency. Scan quality was evaluated using Lift on dark and Noise on dark indices. User experience was measured after each scan with the 10-item System Usability Scale (SUS, 0–100). Within-participant changes were analyzed with paired t tests or Wilcoxon signed-rank tests. Results All operators completed both sessions. Median performance improved from Scan 1 to Scan 2, with total scan time decreasing from 5 min 27 s to 2 min 53 s. The proportion of “Excellent” scans (<5 minutes) increased from 50% to 84%, and “Poor” scans (>10 minutes) fell to zero. Repeat scans per session declined from 38 to 24, and mean scanpath time shortened while becoming more consistent. Lift on dark and Noise on dark remained stable, indicating no degradation in signal quality as operators worked faster. SUS scores improved from 69.4 to 76.5, reflecting higher perceived ease of use and confidence. Conclusions After minimal training, novice operators achieved rapid, reliable NIR scans with faster performance, fewer repeat scans, stable signal quality, and improved usability ratings. This work shows that portable NIR can practically complement CT by helping prioritize transport and focus scarce imaging resources in emergency, sideline, and remote head trauma 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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".