Live Pulse-Echo Speed-of-Sound Estimation for Quality Assessment of Large Muscles in Humans
Bibliographic record
Abstract
OBJECTIVE: Muscle quality is broadly regarded as an indicator of physical health. Conventionally, ultrasound metrics such as echogenicity have been derived to assess muscle quality, but these measures are prone to changes in ultrasound hardware and operator skill. In contrast, tissue speed-of-sound (SoS) is an intrinsic property that can potentially act as a more robust biomarker for muscle quality. In this work, we demonstrate that muscle SoS can be effectively assessed in real-time via a novel pulse-echo ultrasound technique. METHODS: We conducted a study on the biceps, quadriceps, and calf muscles of 40 human volunteers (Sex: 21 M, 19 F; Age 30.3 ± 10.4 years) and compared muscle SoS to anthropometric and demographic factors [body-mass index (BMI), sex, physical activity]. SoS measurements were obtained using an image-guided global SoS estimation tool that was implemented on a portable research scanner (US4R-Lite). RESULTS: Significant linear correlations were observed between BMI and muscle SoS in the biceps (M: r = -0.75, F: r = -0.75), quadriceps (M: r = -0.54, F: r = -0.70), and calf (M: r = -0.59, F: r = -0.77). Unpaired t-tests demonstrated SoS differences between male and female muscles for all muscle groups measured (p < 0.001 for all muscles). A significant correlation was found for males only (biceps: M: r = 0.50, F: r = 0.009) between SoS and self-reported physical activity. CONCLUSION: Our study is the first to report muscle SoS in the biceps and quadriceps, as pulse-echo SoS estimation permits measurement in muscles inaccessible to previous devices. Overall, our study demonstrates the potential for clinical translation and application of the tissue SoS as a robust muscle quality biomarker with real-time performance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".