Investigating Perioperative Heart Migration during Robot-Assisted Coronary Artery Bypass Grafting Interventions
Bibliographic record
Abstract
OBJECTIVE: : For robot-assisted coronary artery bypass graft interventions, surgeons typically use a preoperative thoracic computed tomography scan of the patient to plan the procedure. However, the cardiac anatomy is prone to changes induced perioperatively in the effort to access the heart and surgical targets, which, in turn, may invalidate the initial plan. This article presents a method to estimate the perioperative heart migration, information which can be further exploited to refine the preoperative surgical plan. METHODS: : Tracked transesophageal ultrasound images of four patients' hearts were acquired at each stage in the procedure: before lung deflation, after lung deflation, and after both lung deflation and CO2 thoracic insufflation. Anatomic features of interest-the mitral and aortic valves-were identified from each dataset, and their movement between the different procedure stages was recorded and used to estimate the global heart displacement. Moreover, the local morphology of the features of interest was investigated to provide insight on the extent of the deformation the heart has undergone during the workflow. RESULTS: : The study suggested that the heart does undergo substantial displacement-on the order of 10 to 15 mm in each direction (axial, coronal, and sagittal) after lung deflation and CO2 thoracic insufflation. However, no significant differences (P > 0.1) were observed in the morphologic characteristics of the features of interest across the multiple workflow stages, suggesting that local deformations occur at a much smaller scale compared with the global migration. CONCLUSIONS: : The quantification of the perioperatively induced changes is critical to track the displacement of the heart and surgical targets. The recorded migration patterns should not be ignored but rather be used to update the surgical plan to better suit the intraoperative environment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".