A comparison of five point-of-care ultrasound devices for use in ophthalmology and facial aesthetics
Bibliographic record
Abstract
Introduction: Point-of-care ultrasound is becoming increasingly popular, and we sought to examine its role in evaluating ocular and periocular structures and facial vasculature. With the large number of point-of-care ultrasound devices available, it is difficult to determine which devices may be best suited for ophthalmic and facial aesthetic applications. This study compares five popular handheld point-of-care ultrasound devices to help guide clinicians in choosing the device best suited for their needs. Methods: We compared five point-of-care ultrasound devices: Butterfly IQ+ (Butterfly, Burlington, MA), L15 (Clarius Mobile Health, Vancouver, British Columbia, Canada), L20 (Clarius Mobile Health, Vancouver, British Columbia, Canada), Lumify (Philips, Amsterdam, Netherlands) and Vscan Air (GE, Boston, MA). Three ophthalmologists obtained the following views on three volunteers: eight arteries, four ocular and periocular structures and areas of filler injections. The image quality of each view was graded on a four-point Likert-type scale. In addition, graders filled out a survey. The data were analysed using analysis of variance tests with the significance level set to p < 0.05. Results: In terms of overall image quality, the L20 received the highest mean rating, followed by the L15, Vscan Air, Butterfly IQ+ and the Lumify ( p < 0.05). With further stratification for structure type, the L20 was ranked first for filler, artery and orbital imaging ( p < 0.05). Conclusions: The L20 received the highest image quality rankings. While image quality is an important aspect of point-of-care ultrasound device selection, other factors such as cost, wireless capabilities, range of presets and battery life should also be considered.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".