Robotic acquisition of echocardiographic images, a proof of concept and safety study
Bibliographic record
Abstract
Abstract Background/Introduction Though echocardiography remains the most utilized imaging modality in daily cardiology practice, a major challenge remains the physical strain and injury that can be induced in the echocardiographer. Purpose We developed a prototype robotic arm assisted echocardiography system which incorporates robotics with traditional echocardiography. We assessed the safety and feasibility of the system in 31 volunteers. Methods An ultrasound transducer was mounted upon a robotic arm (UR10 robotic arm, Universal Robots, Odense, Denmark). The transducer was connected to a Philips EPIQ 7C system. The position of the transducer was modified by the echocardiographer who could manipulate a remote stick controller in their hand whilst orientating its position in relation to the real-time images acquired upon the monitor of the echocardiography scanner. This enabled the sonographer to optimize the images and to initiate recordings of parasternal and apical 2D and 3D images. The maximal force on the robot arm was set to 5 Newtons (N) which was deemed to be both safe and to allow for adequate image quality. The force induced by the probe upon the chest wall was measured continuously. Acquired images were assessed for quality ranging from excellent to poor. Results 17 males and 14 females were scanned. The number of images acquired varied from a minimum of 16 and a maximum of 61. The mean time taken to scan an individual was 17.4 minutes with a standard deviation (SD) of 9.9 minutes. This equated to an average of 1.9 images per minute (SD 0.7). No statistical difference was noted between the number of images per minute obtained between male and female participants (mean (+/- SD) for males was 2.1(0.8) and for females 1.8 (0.7) P=0.27). The average force that was applied by the probe was 3.95+/-0.83 Newtons. None of the volunteers reported significant discomfort in the acquisition of the images. Of the 3D images obtained 5 of the participants had poor apical 3D datasets and 3 had poor parasternal datasets but the remaining images acquired were adequate for diagnostic quality. Conclusion(s) Our study demonstrates the feasibility and safety of acquiring 2D and 3D images using a prototype robotic arm assisted echocardiography system. Despite using a low force to the chest wall, the majority of images obtained were of diagnostic quality. Future studies will assess the techniques validity within a routine patient population compared to standard imaging performed by an experienced sonographer. Set up of robot-arm echocardioghy
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".