A system for ultrasound-guided computer-assisted orthopaedic surgery
Bibliographic record
Abstract
Current computer-assisted orthopedic surgery (CAOS) systems typically use preoperative computed tomography (CT) and intraoperative fluoroscopy as their imaging modalities. Because these imaging tools use X-rays, both patients and surgeons are exposed to ionizing radiation that may cause long-term health damage. To register the patient with the preoperative surgical plan, these techniques require tracking of the targeted anatomy by invasively mounting a tracking device on the patient, which results in extra pain and may prolong recovery time. The mounting procedure also leads to a major difficulty of using these approaches to track small bones or mobile fractures. Furthermore, it is practically impossible to mount a heavy tracking device on a small bone, which thus restricts the use of CAOS techniques. This article presents a novel CAOS method that employs 2D ultrasound (US) as the imaging modality. Medical US is non-ionizing and real-time, and our proposed method does not require any invasive mounting procedures. Experiments have shown that the proposed registration technique has sub-millimetric accuracy in localizing the best match between the intraoperative and preoperative images, demonstrating great potential for orthopedic applications. This method has some significant advantages over previously reported US-guided CAOS techniques: it requires no segmentation and employs only a few US images to accurately and robustly localize the patient. Preliminary laboratory results on both a radius-bone phantom and human subjects are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.023 |
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".