Mixed Reality Tele-Ultrasound over 750 km: A Feasibility Study
Bibliographic record
Abstract
To address the lack of access to ultrasound in remote communities, previous work introduced human teleoperation, a mixed reality and haptics-based tele-ultrasound system. In this approach, a novice takes the role of a cognitive robot controlled remotely by an expert through mixed reality. In this manuscript we summarize new developments to this system and describe a feasibility study assessing it’s use for long-distance remote abdominal ultrasound examinations. To provide simple but effective haptic feedback, we used an ellipsoid model of the patient with its parameters calibrated using our system’s position and force sensors. We tested the system in Skidegate, Haida Gwaii, Canada, with the experts positioned 754 km away in Vancouver, Canada. We performed 11 total scans with 10 novices and two sonographers. The sonographers were tasked with acquiring five target images in the epigastric region. The image acquisition quality was assessed by two radiologists. We collected alignment data and the novices completed task load and usability questionnaires. Both the novices and sonographers provided written and verbal feedback to inform future design iterations. Of the acquired images, 92% had sufficient quality for interpretation by both radiologists. For the remaining 8%, quality issues were largely due to body habitus which would have caused difficulties even when scanning in person. The mean task load reported by the novices was below reference values reported in literature and the usability was unanimously positive. No correlation was found between image quality and the follower’s alignment error with the virtual transducer. Overall, we show that human teleoperation enables sonographers to perform remote abdominal ultrasound imaging with high performance, even across large distances and with novice followers. Future work will compare human teleoperation to conventional, robotic and tele-mentored ultrasound.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".