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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".