Evaluation of AR image tracking for AR-guided surgical applications
Bibliographic record
Abstract
Augmented reality (AR) image tracking may be used in AR-guided surgical applications for real-time guidance and quantitative feedback. With AR-guided applications allowing for broader accessibility compared to specialized systems used in traditional surgical image-guidance, we evaluated the measurement errors of monocular AR image tracking against current gold standard infrared optical and electromagnetic (EM) tracking. A measurement stylus was designed and 3D printed, allowing for monocular AR image tracking using a Logitech C920 camera, infrared optical tracking with Northern Digital Inc. (NDI) Vicra, and EM tracking with NDI Aurora through corresponding sensor attachments. A measurement phantom was also designed and 3D printed, consisting of 3 measurement planes with 81 measurement points in each plane, totaling 243 measurement points across a 16 cm x 16 cm x 18 cm measurement volume. Pivot calibration was performed using random sample consensus (RANSAC) sphere fitting to calculate the offsets between sensor attachments to stylus tip across each tracking system. Measurements of the stylus tip were collected across the measurement phantom for each tracking system. Each system’s fiducial registration error was quantified using the collected tip positions through rigid registration between the tracking system and the designed phantom points from CAD. Fiducial registration errors were 1.19 mm, 0.59 mm, and 0.51 mm for monocular AR, infrared optical, and EM tracking. Monocular AR image tracking presents a cost effective and accessible solution for surgical guidance applications. Errors close to 1 mm may be suitable for scenarios such as surgical simulators in competency-based education and AR-based planning.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".