Scaphoid fracture fixation: localization of bones through statistical model to ultrasound registration
Bibliographic record
Abstract
Percutaneous treatment of scaphoid fractures has found increasing interest in recent years as it promises to minimize soft-tissue damage, and minimizes the risk of infections and the loss of the joint stability. However, as this procedure is mostly performed on 2D fluoroscopic images, the accurate localization of the scaphoid bone for fracture fixation renders extremely challenging. In this work, we thus propose the integration of a statistical wrist model with 3D intraoperative ultrasound for accurate localization of the scaphoid bone. We utilize a previously developed statistical wrist model and register it to bone surfaces in ultrasound images using a probabilistic approach that involves expectation-maximization. We utilize local phase symmetry to detect features in noisy ultrasound images; in addition, we use shadow information in ultrasound images to enhance and set apart bone from other features. Feasibility experiments are performed by registering the wrist model to 3D ultrasound volumes of two different wrists at two different wrist positions. And the result indicates a potential of the proposed technique for localization of the scaphoid bone in ultrasound images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".