Integration of trans-esophageal echocardiography with magnetic tracking technology for cardiac interventions
Bibliographic record
Abstract
Trans-esophageal echocardiography (TEE) is a standard component of patient monitoring during most cardiac surgeries. In recent years magnetic tracking systems (MTS) have become sufficiently robust to function effectively in appropriately structured operating room environments. The ability to track a conventional multiplanar 2D TEE transducer in 3D space offers incredible potential by greatly expanding the cumulative field of view of cardiac anatomy beyond the limited field of view provided by 2D and 3D TEE technology. However, there is currently no TEE probe manufactured with MTS technology embedded in the transducer, which means sensors must be attached to the outer surface of the TEE. This leads to potential safety issues for patients, as well as potential damage to the sensor during procedures. This paper presents a standard 2D TEE probe fully integrated with MTS technology. The system is evaluated in an environment free of magnetic and electromagnetic disturbances, as well as a clinical operating room in the presence of a da Vinci robotic system. Our first integrated TEE device is currently being used in animal studies for virtual reality-enhanced ultrasound guidance of intracardiac surgeries, while the "second generation" TEE is in use in a clinical operating room as part of a project to measure perioperative heart shift and optimal port placement for robotic cardiac surgery. We demonstrate excellent system accuracy for both applications.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".