Building Your Future Holographic Mentor: Can We Use Mixed Reality Holograms for Visual Spatial Motor Skills Acquisition in Surgical Education?
Bibliographic record
Abstract
Learning surgical skills require critical visual-spatial motor skills. Current learning methods employ costly and limited in-person teaching in addition to supplementation by videos, textbooks, and cadaveric labs. Increasingly limited healthcare resources and in-person training has led to growing concerns for skills acquisition of trainees. Recent Mixed Reality (MR) devices offer an attractive solution to these resource barriers by providing three-dimensional holographic representations of reality that mimic in-person experiences in a portable, individualized, and cost-effective form. We developed and evaluated two holographic MR models to explore the feasibility of visual-spatial motor skill acquisition from a technical development, learning, and usability perspective. In our first, a pair of holographic hands were created and projected in front of the trainee, and participants were evaluated on their ability to learn complex hand motions in comparison to traditional methods of video and apprenticeship-based learning. The second model displayed a 3D holographic model of the middle and inner ear with labeled anatomical structures which users could explore and user experience feedback was obtained. Our studies demonstrated that scores between MR and apprenticeship learning were comparable. All felt MR was an effective learning tool and most noted that the MR models were better than existing didactic methods of learning. Identified advantages of MR included the ability to provide true 3D spatial representation, improved visualization of smaller structures in detail by upscaling the models, and improved interactivity. Our results demonstrate that holographic learning is able to mimic in-person learning for visual-spatial motor skills and could be a new effective form of self-directed apprenticeship learning.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".