Impact of calcification modeling to improve image fusion accuracy for endovascular aortic aneurysm repair
Why this work is in the frame
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Bibliographic record
Abstract
Since the 1990s, endovascular aortic aneurysm repair (EVAR) has become a common alternative to open surgery for the treatment of abdominal aortic aneurysms (AAAs). To aid the deployment of stent-grafts, fluoroscopic image guidance can be enhanced using preoperative simulation and intraoperative image fusion techniques. However, the impact of calcification (Ca) presence on the guidance accuracy of such techniques is yet to be considered. In the present work, we introduce a guidance tool that accounts for patient-specific Ca presence. Numerical simulations of EVAR were developed for 12 elective AAA patients, both with (With-Ca) and without (No-Ca) Ca consideration. To assess the accuracy of the simulations, the image results were overlaid on corresponding intraoperative images and the overlay error was measured at selected anatomical landmarks. With this approach we gained insight into the impact of Ca presence on image fusion accuracy. Inclusion of Ca improved mean image fusion accuracy by 8.68 ± 4.59%. In addition, a positive correlation between the relative Ca presence and the image fusion accuracy was found (R = .753, p < .005). Our results suggest that considering Ca presence in patient-specific EVAR simulations increases the reliability of EVAR image guidance techniques that utilize numerical simulation, especially for patients with severe aortic Ca presence.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it