Towards a First-Person Perspective Mixed Reality Guidance System for Needle Interventions
Bibliographic record
Abstract
While ultrasound (US) guidance has been used during central venous catheterization to reduce complications, including the puncturing of arteries, the rate of such problems remains non-negligible. To further reduce complication rates, mixed-reality systems have been proposed as part of the user interface for such procedures. We demonstrate the use of a surgical navigation system that renders a calibrated US image, and the needle and its trajectory, in a common frame of reference. We compare the effectiveness of this system, whereby images are rendered on a planar monitor and within a head-mounted display (HMD), to the standard-of-care US-only approach, via a phantom-based user study that recruited 31 expert clinicians and 20 medical students. These users performed needle-insertions into a phantom under the three modes of visualization. The success rates were significantly improved under HMD-guidance as compared to US-guidance, for both expert clinicians (94% vs. 70%) and medical students (70% vs. 25%). Users more consistently positioned their needle closer to the center of the vessel's lumen under HMD-guidance compared to US-guidance. The performance of the clinicians when interacting with this monitor system was comparable to using US-only guidance, with no significant difference being observed across any metrics. The results suggest that the use of an HMD to align the clinician's visual and motor fields promotes successful needle guidance, highlighting the importance of continued HMD-guidance research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".