Depth Camera Augmented Fluoroscopy with Video Overlay
Bibliographic record
Abstract
In many orthopedic surgeries, the surgeon relies on a C-arm fluoroscopy machine with the images usually displayed on a bedside monitor. The mental effort that surgeons expend transferring information from the imaging display back to the surgical site can lead to distraction causing errors that could directly influence quality of surgery. Depth Camera Augmented Fluoroscopy (DeCAF) uses an Intel RealSense depth camera to provide real-time visualization of the surgical site by overlaying x-ray images from the C-arm onto live video of the patient’s surface anatomy. Using geometric data acquired via the depth camera, the device facilitates transforming a real-time video feed aligned with the camera coordinate system to a perspective aligned with the x-ray source. The x- ray overlay is attained while restricting incursion on the surgeon’s work area and allowing the C-arm to be used in its normal position to minimize radiation exposure. DeCAF successfully facilitates an x-ray video overlay feature while eliminating key limitations such as size, radiation exposure and acquisition time associated with other similar devices. Future work will involve evaluating overlay accuracy, the addition of second depth camera to aid in filling in areas with missing details, and a design iteration involving bagging of the camera with a sterile cover to ensure compliance with asepsis requirements prior to evaluating the system in the operating room.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".