Augmented Reality and Human Factors Applications for the Neurosurgical Operating Room
Bibliographic record
Abstract
The virtual overlay of patient-specific anatomies onto a surgical site through Augmented Reality (AR) technologies has been thought to be a potentially ideal neuronavigational system for use in neurosurgery. Although impressive and futuristic, there are many design considerations that must be taken into account, including surgeon reception, perceived utility, intuitive control and manipulation design, and overall system accuracy during surgery. To implement AR into the neurosurgical Operating Room (OR), a gradual approach of evolutionary design to ensure widespread adoption may be considered. This thesis presents a potential pathway for the introduction of AR technologies into the neurosurgical OR. The thesis is divided into three parts: incorporation of AR features into existing platforms for improved functionality and introduction of AR concepts to surgical environments, observation and evaluation of surgeon perception of AR overlays and AR headsets to inform display methods and designs, and quantification of virtual object placement accuracy in a clinical environment. The findings presented show that AR integrated systems improve OR workflow when conventional tracked tools are unavailable, user preference of AR overlays onto the surgical site change depending on operator experience level, and the placement accuracy of state-of-the-art AR head mounted displays are suitable for presurgical planning and very close to accuracy needed for surgical guidance. These three elements are key to developing a pathway for adoption of AR technologies in the OR, and help to inform designs for future headsets to assist surgeons and improve patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".