Force Controlled Operation of Robotic Arm for Echocardiography Scanning
Bibliographic record
Abstract
Echocardiography or ultrasound imaging of the heart is an invaluable tool for non-invasive cardiac evaluation. It is inexpensive, portable, and safe due to its lack of ionizing radiation. However, prolonged manual operation introduces musculoskeletal strain to sonographers, which requires innovative solutions. Collaborative robots or cobots offer an excellent option to reduce strain during cardiac scans. However, precise force control is vital to ensure patient comfort and optimal image quality. This study evaluates the application of force during robotic echocardiography scans in 30 healthy volunteers, demonstrating significantly reduced force compared to manual scans. Importantly, image quality remains uncompromised despite the minimal force exerted. These findings underscore the potential for robotic systems to be introduced into clinical practice by improving patient care and alleviating the stress of the sonographer. Future research could explore the application of robotic echocardiography in diverse patient populations, including those with various cardiac pathologies, to further validate its efficacy and safety. Overall, this study marks a significant step towards improving cardiac imaging practices and improving healthcare outcomes through the implementation of advanced robotic technologies.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".