Augmented Reality Visualization with Use of Image Overlay Technology for MR Imaging–guided Interventions: Assessment of Performance in Cadaveric Shoulder and Hip Arthrography at 1.5 T
Bibliographic record
Abstract
PURPOSE: To prospectively assess overlay technology in providing accurate and efficient targeting for magnetic resonance (MR) imaging-guided shoulder and hip joint arthrography. MATERIALS AND METHODS: A prototype augmented reality image overlay system was used in conjunction with a clinical 1.5-T MR imager. A total of 24 shoulder joint and 24 hip joint injections were planned in 12 human cadavers. Two operators (A and B) participated, each performing procedures on different cadavers using image overlay guidance. MR imaging was used to confirm needle positions, monitor injections, and perform MR arthrography. Accuracy was assessed according to the rate of needle adjustment, target error, and whether the injection was intraarticular. Efficiency was assessed according to arthrography procedural time. Operator differences were assessed with comparison of accuracy and procedure times between the operators. Mann-Whitney U test and Fisher exact test were used to assess group differences. RESULTS: Forty-five arthrography procedures (23 shoulders, 22 hips) were performed. Three joints had prostheses and were excluded. Operator A performed 12 shoulder and 12 hip injections. Operator B performed 11 shoulder and 10 hip injections. Needle adjustment rate was 13% (six of 45; one for operator A and five for operator B). Target error was 3.1 mm±1.2 (standard deviation) (operator A, 2.9 mm±1.4; operator B, 3.5 mm±0.9). Intraarticular injection rate was 100% (45 of 45). The average arthrography time was 14 minutes (range, 6-27 minutes; 12 minutes [range, 6-25 minutes] for operator A and 16 minutes [range, 6-27 min] for operator B). Operator differences were not significant with regard to needle adjustment rate (P=.08), target error (P=.07), intraarticular injection rate (P>.99), and arthrography time (P=.22). CONCLUSION: Image overlay technology provides accurate and efficient MR guidance for successful shoulder and hip arthrography in human cadavers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".