Hybrid reflected‐ultrasound computed tomography versus B‐mode‐ultrasound for muscle scoring in spinal muscular atrophy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Novel light- and sound-based technologies like multispectral optoacoustic tomography (MSOT) with co-registered reflected-ultrasound computed tomography (RUCT) could add additional value to conventional ultrasound (US) for disease phenotyping in pediatric spinal muscular atrophy (SMA). The aim of this study was to investigate the quality of RUCT compared to US for qualitative and quantitative assessment of imaging neuromuscular disorders. METHODS: Subanalyzing the MSOT SMA study, 288 RUCT and 276 US images from 10 SMA patients (mean age 9.0 ± 3.7) and 10 gender- and age-matched healthy volunteers (HV; mean age 8.7 ± 4.3) were analyzed for quantitative (grayscale levels [GSLs]) and qualitative (echogenicity, distribution pattern, Heckmatt scale, and muscle texture) muscle changes. RUCT and US measures were further correlated with clinical standard motor outcomes. RESULTS: Quantitative agreement using GSLs revealed significantly higher GSLs in muscles of SMA patients compared to healthy muscles in both techniques (US mean GSL [SD] SMA vs. HV: 110.70 [27.8] vs. 68.85 [19.2], p < .0001; RUCT mean GSL [SD] SMA vs. HV: 91.81 [21.8] vs. 59.86 [8.2], p < .0001) with good correlation with motor outcome tests, respectively. Qualitative agreement between methods for muscle composition was excellent for differentiation of pathological versus healthy muscles, echogenicity, and distribution pattern, moderate for Heckmatt scale, and poor for muscle texture. CONCLUSIONS: The data suggest that RUCT may allow the assessment of basic qualitative and quantitative measures for muscular diseases with comparable results to conventional US.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".