A Mixed Reality–Based Telesupervised Ultrasound Education Platform on 5G Network Compared to Direct Supervision: Prospective Randomized Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Ultrasound education is transitioning from in-person training to remote methods using mixed reality (MR) and 5G networks. Previous studies are mainly experimental, lacking randomized controlled trials in direct training scenarios. OBJECTIVE: This study aimed to compare an MR-based tele-supervised ultrasound education platform on private 5G networks with traditional in-person training for novice doctors. METHODS: Conducted at a tertiary academic hospital from November to December 2023, the prospective unblinded randomized controlled pilot study assigned doctors without prior abdominal ultrasound education experience to either the tele-supervision group (TG; n = 20) or direct supervision group (DG; n = 20). Participants received a 15-min video lecture, conducted ultrasound on a phantom, and had 18 images scored by two blinded experts. Additionally, the TG received five minutes of training on basic operation of a head-mounted display (HMD). Communication between doctors in the TG and supervisors was facilitated through an HMD, whereas those in the DG interacted directly with supervisors. Primary outcomes were image quality scores, while secondary outcomes included procedure time, number of supervisor interventions, user experience using NASA-Task load index (NASA-TLX), System Usability Scale (SUS), and self-confidence through pre- and post-surveys. RESULTS: Image quality scores and procedure times showed no significant differences between the groups (TG: 66.8 ± 10.3 vs DG: 66.8 ± 10.4, P = .844; TG: 23.8 ± 8.0 min vs DG: 24.0 ± 8.1 min, P = .946). However, the TG engaged in more educational interventions (TG: 4.0 ± 2.5 vs DG: 0.8 ± 1.1, P <.001), reflecting a more interactive training environment. TG participants reported lower NASA-TLX scores for mental demand (43.8 ± 24.8 vs 60.6 ± 22.4, P = .03), effort (43.1 ± 22.9 vs 67.9 ± 17, P < .001), and frustration (26.9 ± 20.3 vs 45.2 ± 27.8, P = .022), indicating a reduced cognitive load compared the DG. The mean SUS score was also higher in the TG (66.6 ± 9.1 vs 60.2 ± 10.4, P =.046), suggesting better usability. Both groups showed significant improvements in confidence, with the TG showing notably greater improvement in abdominal ultrasound proficiency (Pre-education ━ TG: 1.6 ± 0.9 vs DG: 1.7 ± 0.9, P =.728; Post-education ━ TG: 3.8 ± 0.9 vs DG: 2.8 ± 1.0, P =.006). CONCLUSIONS: Although no significant differences in image quality scores were observed between groups, considerable differences in positive educational interactions, workload, and usability were evident. These findings emphasize the platform's potential to enhance the ultrasound training experience, suggesting more interactive and efficient learning. CLINICALTRIAL: ClinicalTrials.gov, NCT06171828.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".