Evaluation of Model-Enhanced Ultrasound-Assisted Interventional Guidance in a Cardiac Phantom
Bibliographic record
Abstract
Minimizing invasiveness associated with cardiac procedures has led to limited visual access to the target tissues. To address these limitations, we have developed a visualization environment that integrates interventional ultrasound (US) imaging with preoperative anatomical models and virtual representations of the surgical instruments tracked in real time. In this paper, we present a comprehensive evaluation of our model-enhanced US-guidance environment by simulating clinically relevant interventions in vitro . We have demonstrated that model-enhanced US guidance provides a clinically desired targeting accuracy better than 3-mm rms and maintains this level of accuracy even in the case of image-to-patient misalignments that are often encountered in the clinic. These studies emphasize the benefits of integrating real-time imaging with preoperative data to enhance surgical navigation in the absence of direct vision during minimally invasive cardiac interventions.
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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 | 0.000 |
| 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 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".