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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".