Ultrasound Shear Wave Velocity of Peripheral Nerves: A Possible Non‐Invasive Biomarker for Demyelinating Neuropathies
Bibliographic record
Abstract
BACKGROUND AND AIMS: Repeated cycles of demyelination and remyelination alter nerve tissue composition, likely affecting its material properties, including stiffness. Using ultrasound shear wave elastography (SWE), we assessed nerve shear wave velocity (SWV), a surrogate measure of stiffness, to determine its potential as a biomarker for demyelinating neuropathies, including chronic inflammatory demyelinating polyneuropathy, Charcot-Marie-Tooth type 1A, and anti-myelin-associated glycoprotein neuropathy. METHODS: This cross-sectional study compared nerve SWV between 20 patients with demyelinating neuropathies (60.2 ± 13.1 years) and 16 age-matched controls (56.8 ± 10.8 years). Each participant underwent bilateral SWE of the proximal and distal segments of four peripheral nerves in the upper (median, ulnar and radial) and lower (sciatic-tibial) limbs. Measurements were conducted in different limb positions to mimic two nerve tensile states, yielding a total of 32 nerve stiffness measurements per participant. Conventional nerve cross-sectional area was further evaluated for each nerve and location. RESULTS: Individuals with demyelinating polyneuropathy exhibited increased nerve SWV compared to age-matched controls (mean difference = 0.7 m/s, 95%CI [0.5 to 0.9]; p < 0.0001). This difference was observed across all nerves and regions, with the largest difference noted in the tibial. Axial nerve tension amplified these differences. Additionally, moderate to high negative correlations were observed between motor nerve conduction and nerve SWV. INTERPRETATION: This study identifies significant neuropathy-associated alterations in peripheral nerve elasticity. Our findings suggest that nerve stiffness could be a promising biomarker for demyelinating neuropathies, and provide a basis for the development of standardized peripheral nerve SWE protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".