A Fast, Accurate and Easy to Implement Method for Pose Recognition of an Intramedullary Nail using a Tracked C-arm
Bibliographic record
Abstract
Abstract. A C-arm is a mobile X-ray device that is frequently used during orthopaedic surgeries. It consists of a semi-circular, arc-shaped arm that holds an X-ray transmitter at one end and an X-ray detector at the other. Intramedullary nail (IM nail) fixation is a popular orthopaedic surgery in which a metallic rod is placed into the patient's fractured bone (femur or tibia) and fixed using metal screws. The main challenge of IM-nail fixation surgery is to achieve the X-ray shot in which the distal holes of the IM nail appear as circles (desired view) so that the surgeon can easily insert the screws. Although C-arm X-ray devices are routinely used in IM-nail fixation surgeries, the surgeons or radiation technologists (rad-techs) usually use it in a trial-and-error manner. This method raises both radiation exposure and surgery time. In this study, we have designed and developed an IM-nail distal locking navigation technique that leads to more accurate and faster screw placement with a lower radiation dose and a minimum number of added steps to the operation to make it more accepted within the orthopaedic community. The specific purpose of this study was to develop and validate an automated technique for identifying the current pose of the IM nail relative to the C-arm. An accuracy assessment was performed to test the reliability of the navigation results. Translational accuracy was demonstrated to be better than 1 mm, roll and pitch rotations better than 2° and yaw rotational accuracy better than 2–5° depending on the separate angle. Computation time was less than 3.5 seconds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".