SurgiKLAR: An Augmented Reality Framework for Improved Surgical Training
Bibliographic record
Abstract
Augmented reality (AR) technology is transforming minimally invasive surgeries by merging physical and virtual spaces to improve intraoperative guidance and training. We present SurgiKLAR, a mixed reality framework for improved surgical training, to clearly (“klar”) visualize segmented anatomies from preoperative scans coregistered with patient-specific models to simulate surgical procedures. The system is adapted for gynecology surgeries, featuring realistic uterine models and instruments for adaptive simulations, personalized guidance, and real-time alerts. Preliminary evaluations with a 3D-printed phantom demonstrated its potential application in preplanning complex scenarios. We conducted a two-phase user study to evaluate the usability of the SurgiKLAR system. Phase I assessed AR adoption challenges, and Phase II validated usability for training. The user feedback highlighted the importance of accurate visualizations and the need for extensive training programs. Future work involves an extension to diverse surgical domains, improved precision, and enhanced safety, thereby highlighting the transformative potential of cross-reality in surgery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".