Usability of a real-time tracked augmented reality display system in musculoskeletal injections
Bibliographic record
Abstract
PURPOSE: Image-guided needle interventions are seldom performed with augmented reality guidance in clinical practice due to many workspace and usability restrictions. We propose a real-time optically tracked image overlay system to make image-guided musculoskeletal injections more efficient and assess its usability in a bed-side clinical environment. METHODS: An image overlay system consisting of an optically tracked viewbox, tablet computer, and semitransparent mirror allows users to navigate scanned patient volumetric images in real-time using software built on the open-source 3D Slicer application platform. A series of experiments were conducted to evaluate the latency and screen refresh rate of the system using different image resolutions. To assess the usability of the system and software, five medical professionals were asked to navigate patient images while using the overlay and completed a questionnaire to assess the system. RESULTS: In assessing the latency of the system with scanned images of varying size, screen refresh rates were approximately 5 FPS. The study showed that participants found using the image overlay system easy, and found the table-mounted system was significantly more usable and effective than the handheld system. CONCLUSION: It was determined that the system performs comparably with scanned images of varying size when assessing the latency of the system. During our usability study, participants preferred the table-mounted system over the handheld. The participants also felt that the system itself was simple to use and understand. With these results, the image overlay system shows promise for use in a clinical 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".