Ultrasound Image Quality Comparison Between a Handheld Ultrasound Transducer and Mid-Range Ultrasound Machine
Bibliographic record
Abstract
Objectives: Not all labor and delivery floors are equipped with ultrasound machines which can serve the needs of both obstetricians and anesthesiologists. This cross-sectional, blinded, randomized observational study compares the image resolution (RES), detail (DET), and quality (IQ) acquired by a handheld ultrasound, the Butterfly iQ, and a mid-range mobile device, the Sonosite M-turbo US (SU), to evaluate their use as a shared resource. Methods: Seventy-four pairs of ultrasound images were obtained for different imaging purposes: 29 for spine (Sp), 15 for transversus abdominis plane (TAP) and 30 for diagnostic obstetrics (OB) purposes. Each location was scanned by both the handheld and mid-range machine, resulting in 148 images. The images were graded by three blinded experienced sonographers on a 10-point Likert scale. Results: The mean difference for Sp imaging favored the handheld device (RES: -0.6 [(95% CI -1.1, -0.1), p = 0.017], DET: -0.8 [(95% CI -1.2, -0.3), p = 0.001] and IQ: -0.9 [95% CI-1.3, -0.4, p = 0.001]). For the TAP images, there was no statistical difference in RES or IQ, but DET was favored in the handheld device (-0.8 [(95% CI-1.2, -0.5), p < 0.001]). For OB images, the SU was favored over the handheld device with RES, DET and IQ with mean differences of 1.7 [(95% CI 1.2, 2.1), p < 0.001], 1.6 [(95% CI 1.2, 2.0], p < 0.001] and 1.1 [(95% CI 0.7, 1.5]), p < 0.001), respectively. Conclusions: Where resources are limited, a handheld ultrasound may be considered as a potential low-cost alternative to a more expensive ultrasound machine for point of care ultrasonography, better suited to anesthetic vs. diagnostic obstetrical indications.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".