Feasibility of Robotic-Assisted Echocardiography in Patients: Reducing Sonographer Strain without Compromising Image Quality
Bibliographic record
Abstract
Echocardiography remains a widely used imaging modality for the evaluation of cardiac structure and function. Despite its diagnostic value, conventional manual scanning techniques require sonographers to maintain repetitive postures and apply sustained pressure over extended periods, increasing the risk of work-related musculoskeletal disorders. In recent years, collaborative robots, or cobots, have emerged as a promising solution for applications requiring robots to operate safely alongside human operators. Modern cobot systems are often equipped with integrated force and torque sensors, enabling precise control of contact forces during patient scanning to ensure both safety and comfort. This study investigates the feasibility of a robotic-assisted echocardiography system in a clinical setting, focusing on its potential to reduce physical strain on sonographers while maintaining diagnostic image quality. A patient-based evaluation over 24 participants was conducted to assess system performance, force control accuracy, and image quality compared to conventional manual scanning. The findings aim to provide insights into the integration of robotic assistance in echocardiography workflows, with implications for improving operator ergonomics, patient safety, and imaging quality.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".