Is remotely supervised ultrasound (tele-ultrasound) inferior to the traditional service model of ultrasound with an in-person imaging specialist? A systematic review
Bibliographic record
Abstract
BACKGROUND: Tele-ultrasound is known to offer potential benefits such as improved access and cost efficiency, but concerns still exist about image quality, operator skill, and data security. This study aimed to determine whether remotely supervised ultrasound is inferior to traditional in-centre ultrasound with an in-person imaging specialist regarding patient care quality, service quality, and access to care. METHODS: A systematic search for a critical appraisal of relevant peer-reviewed published literature, as well as a jurisdictional scan of relevant regulations and standards in other Canadian jurisdictions, was performed. RESULTS: Of the original 6051 discrete records identified through the search, 18 studies were selected for inclusion in the review. They originated from 11 countries, and the patient populations spanned infants, children, adults, and pregnant women. The medical applications were echocardiography (including fetal), obstetrical ultrasound, breast ultrasound, thyroid ultrasound, and abdominal ultrasound. The distance between the tele-ultrasound site and the reference site ranged from 23 to 365 km, or a 30 to 45-min drive. In 3 studies, tele-ultrasound images were acquired in one country (India, Peru) and interpreted in another (US or UK). The majority of studies reported good diagnostic accuracy (the proportion of agreement between tele-ultrasound and in-centre ultrasound ranged from 43.4% to 100%, sensitivity ranged from 43% to 97%, and specificity ranged from 77.4% to 100% across studies and tele-ultrasound application). Details are displayed in Supplementary Table 2. There was limited evidence on patients' and providers' perspectives on tele-ultrasound, but in the studies identified, more than half of the patients surveyed felt that tele-ultrasound was acceptable. Additionally, all comments from providers were positive, including their perspectives on the value of tele-ultrasound. The image quality results were mixed. Some studies found that image quality ranged from at least sufficient quality for diagnosis to excellent. However, some other studies reported inadequate image quality in up to 36.8% of cases. It is possible that this range of responses may be due to the varying technical ability/capacity of local tele-ultrasound systems to acquire and transmit images to a remote reader. Cost savings associated with tele-ultrasound were also reported and attributed mainly to travel costs for patients. CONCLUSION: There is no consistent evidence that tele-ultrasound is inferior to in-centre ultrasound, although further high-quality studies are needed.
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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 | 0.018 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".