Development of augmented reality technology for surgical resection accuracy via improved visualization
Bibliographic record
Abstract
A challenge in arthroscopic hip surgery is visualizing patient anatomy beneath the skin's surface, specifically in femoroacetabular impingement (FAI) surgeries. FAI is characterized by a bony deformity and hip pain that leads to osteoarthritis. Due to both poor visualization and under-resection of the bone deformity in FAI surgeries, patients often undergo revision surgeries and/or ongoing pain. During surgery, the patient's preoperative medical images are displayed on a monitor alongside the arthroscope's view. Rather than the surgeon mentally fusing the medical images of the hip anatomy onto the patient, augmented reality (AR) could integrate surgical visualization through a head-mounted display that overlays the patient's virtual anatomy onto the real world. This work presents the results from a preliminary user study, which assessed the functionality and accuracy of our AR live-resection tracking model via Microsoft Hololens 2 with a motion capture (MoCap) system. Our primary objective was to assess the initial accuracy of our live-resection tracking model in a simplified simulation with a physical object compared to our ability to track the resection in a virtual object. Our secondary objective was to obtain user feedback on the current AR system for resection tracking.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".