X-ray vision: the accuracy and repeatability of a technology that allows clinicians to see spinal X-rays superimposed on a person's back
Bibliographic record
Abstract
OBJECTIVE: Since the discovery of ionizing radiation, clinicians have evaluated X-ray images separately from the patient. The objective of this study was to investigate the accuracy and repeatability of a new technology which seeks to resolve this historic limitation by projecting anatomically correct X-ray images on to a person's skin. METHODS: A total of 13 participants enrolled in the study, each having a pre-existing anteroposterior lumbar X-ray. Each participant's image was uploaded into the Hololens Mixed reality system which when worn, allowed a single examiner to view a participant's own X-ray superimposed on the participant's back. The projected image was topographically corrected using depth information obtained by the Hololens system then aligned via existing anatomic landmarks. Using this superimposed image, vertebral levels were identified and validated against spinous process locations obtained by ultrasound. This process was repeated 1-5 days later. The projection of each vertebra was deemed to be "on-target" if it fell within the known morphological dimensions of the spinous process for that specific vertebral level. RESULTS: The projection system created on-target projections with respect to individual vertebral levels 73% of the time with no significant difference seen between testing sessions. The average repeatability for all vertebral levels between testing sessions was 77%. CONCLUSION: These accuracy and repeatability data suggest that the accuracy and repeatability of projecting X-rays directly on to the skin is feasible for identifying underlying anatomy and as such, has potential to place radiological evaluation within the patient context. Future opportunities to improve this procedure will focus on mitigating potential sources of error.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".