Integration of Robotic Technology for Combining Multiple Views in Three-Dimensional Echocardiography
Bibliographic record
Abstract
Echocardiography is one of the most widely used imaging modalities to diagnose cardiac disease. Although two-dimensional echocardiography is widely used, real-time three-dimensional (3D) echocardiography allows for scanning the heart in 3D and significantly improves the field of view. Despite the field of view improvement, the entire heart cannot be imaged in a single 3D echocardiography scan in most cases, and further improvements are needed to solve the problem. This study proposes a robotic arm-based multiview echocardiography fusion system to solve the field-of-view problem by tracking the transducer attached to the arm. In the proposed method, the cardiac structures of human participants are imaged from multiple positions using a 3D echocardiography scanning system. A preliminary evaluation of the system was performed with three volunteer participants. The alignment accuracies of multiple scans were evaluated by delineating the left ventricle in each scan and measuring the overlap between the first scan and the rest. The results demonstrate that the proposed system significantly improves the accuracy of the alignment when images are transformed using tracking information compared to keeping them in their original image-based coordinate system. Future work will be devoted to solving alignment issues related to patient movement and respiration.
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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 | 0.000 | 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".