An Integrated System Using Ultrasound-Based Registration for Surgical Navigation of Iliosacral Screw Insertions
Bibliographic record
Abstract
OBJECTIVE: Pelvic fractures often require fixation through the iliosacral joint, typically guided by fluoroscopy using an untracked C-arm device. However, this involves ionizing radiation exposure and potentially inaccurate screw placement. We introduce the Navigated Orthopaedic Fixations using Ultrasound System (NOFUSS), a radiation-free ultrasound (US)-based end-to-end system for providing real-time navigation for iliosacral screw (ISS) insertions. METHODS: We performed surgeries on 8 human cadaver specimens, inserting four ISSs per specimen to directly compare NOFUSS against conventional fluoroscopy. Six specimens yielded usable (marginal or adequate quality) US images. RESULTS: The median screw entry error, midpoint error, and angulations errors for NOFUSS were 8.4 mm, 7.0 mm, and 1.4, compared to 7.5 mm ( = 0.52), 5.7 mm ( = 0.30), and 4.4 ( = 0.001) for fluoroscopy respectively. NOFUSS resulted in 6 (50%) breaches, compared to 2 (16.7%) in fluoroscopy ( = 0.19). The median insertion time was 7 m 37 s and 12 m 36 s per screw for NOFUSS and fluoroscopy respectively ( = 0.002). The median radiation exposure during the fluoroscopic procedure was 2 m 44 s, (range: 1 m 44 s-3 m 18 s), with no radiation required for NOFUSS. When considering the three cadavers that yielded only adequate-quality US images (12 screws), the measured entry errors were 3.6 mm and 8.1 mm respectively for NOFUSS and fluoroscopy ( = 0.06). CONCLUSION: NOFUSS achieved insertion accuracies on par with the conventional fluoroscopic method, and reduced insertion times and radiation exposure significantly. SIGNIFICANCE: This study demonstrated the feasibility of an automated, radiation-free, US-based surgical navigation system for ISS insertions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".