Ultrasound image resolution influences analysis of skeletal muscle composition
Bibliographic record
Abstract
INTRODUCTION: Analysis of muscle composition using ultrasound requires standardization of several equipment settings (i.e. gain). However, the influence of image resolution, which is altered by imaging depth, on measures of muscle composition is unknown. METHODS: We analysed rectus femoris muscle composition using ultrasound images captured from 32 males and females (aged 28 ± 5 years) at depths of 9.0, 7.3, 5.9 and 4.7 cm. The transducer's orientation was fixed using a clamp during image acquisition to minimize movement. Across each image resolution, a region of interest encompassing the same anatomical area within the muscle was used for muscle composition analysis. Muscle composition was analysed using a combination of first-, second- and higher-order texture features. Muscle composition agreement across image resolutions was evaluated using a one-way ANOVA and intraclass correlation coefficients (ICC). RESULTS: Most muscle composition features displayed differences due to image resolution (p < .05). ICCs demonstrated poor-to-good agreement across different image resolutions. In general, higher resolution images (i.e. shallower imaging depth) demonstrated better agreement (ICC > 0.90) compared to lower resolution images. CONCLUSIONS: Ultrasound image resolution influences muscle composition analysis. Image resolution should be fixed within and between individuals when evaluating muscle composition using ultrasound.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".