A Deep Learning Approach for Single Shot C-Arm Pose Estimation
Bibliographic record
Abstract
During a typical fluoroscopic guided surgery, it is common to acquire multiple x-ray images to correctly position the C-arm. This can be a long process resulting in an in- crease in operation time and ionizing radiation exposure. Our purpose in this study is to implement a machine learning system for predicting the position of the C-arm based on the intraoperative radiographs. The prediction is achieved by training a Deep Learning Network based on Digitally Reconstructed Radiographs. We first showed a high prediction accuracy (4.5 mm and 1.1o) when patient-specific training was implemented. Additionally, we demonstrated a similar range of accuracy by applying transfer-learning on the last lay- ers of the network while reducing the processing time by 83%. In conclusion, in this study, we propose a C-arm position prediction system based on machine learning that can po- tentially reduce the number of intraoperatively acquired X-rays in a common orthopaedic surgical procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".