How to set up a low cost tele-ultrasound capable videoconferencing system with wide applicability
Bibliographic record
Abstract
BACKGROUND: Worldwide ultrasound equipment accessibility is at an all-time high, as technology improves and costs decrease. Ensuring that patients benefit from more accurate resuscitation and diagnoses from a user-dependent technology, such as ultrasound, requires accurate examination, typically entailing significant training. Remote tele-mentored ultrasound (RTUS) examination is, however, a technique pioneered in space medicine that has increased applicability on earth. We, thus, sought to create and demonstrate a cost-minimal approach and system with potentially global applicability. METHODS: The cost-minimal RTUS system was constructed by utilizing a standard off-the-shelf laptop computer that connected to the internet through an internal wireless receiver and/or was tethered through a smartphone. A number of portable hand-held ultrasound devices were digitally streamed into the laptop utilizing a video converter. Both the ultrasound video and the output of a head-mounted video camera were transmitted over freely available Voice Over Internet Protocol (VOIP) software to remote experts who could receive and communicate using any mobile device (computer, tablet, or smartphone) that could access secure VOIP transmissions from the internet. RESULTS: The RTUS system allowed real-time mentored tele-ultrasound to be conducted from a variety of settings that were inside buildings, outside on mountainsides, and even within aircraft in flight all unified by the simple capability of receiving and transmitting VOIP transmissions. . Numerous types of ultrasound examinations were conducted such as abdominal and thoracic examinations with a variety of users mentored who had previous skills ranging from none to expert. Internet connectivity was rarely a limiting factor, with competing logistical and scheduling demands of the participants predominating. CONCLUSIONS: RTUS examinations can educate and guide point of care clinical providers to enhance their use of ultrasound. The scope of the examinations conducted is limited only by the time available and the criticality of the subject being examined. As internet connectivity will only improve worldwide, future developments need to focus on the human factors to optimize tele-sonographic interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".