Wireless vs. traditional ultrasound assessed knee cartilage outcomes utilizing automated gain and normalization techniques
Bibliographic record
Abstract
Advancements in wireless ultrasound technology allow for point of care cartilage imaging, yet validation against traditional ultrasound units remains to be established for knee cartilage outcomes. Therefore, the purpose of our study was to establish the replicability, reliability and agreement, of articular cartilage thickness and echo-intensity measures between traditional and wireless ultrasound units utilizing automatic-gain and normalization techniques. We used traditional and wireless ultrasound to assess the femoral cartilage via transverse suprapatellar scans with the knee in maximum flexion in 71 female NCAA Division I athletes (age: 20.0±1.3 years, height: 171.7±8.7 cm, mass: 69.4±11.0 kg). Wireless ultrasound images (auto-gain and standard gain) were compared to traditional ultrasound images (standard gain) before and after normalization. Ultrasound image pixel values were algebraically scaled to normalize differences between units in image acquisition. Mean thickness and echo-intensity of the global and sub-regions of interest were measured across imaging parameters. Intraclass correlation coefficients (ICC 2, k ) for reliability, standard error of the measurement, minimum detectable difference, and Bland-Altman plots for agreement were calculated between ultrasound units across imaging parameters. Cartilage thickness demonstrated good to excellent reliability (ICC 2,k =0.83–0.95) and minimal bias (-0.06–0.03 mm), in all regions regardless of gain and normalization. However, mean echo-intensity demonstrated poor to moderate reliability (ICC 2,k =0.23–0.68) and moderate bias (-9.8–6.5 au) in all regions, regardless of gain and normalization. While there was a high level of replicability between units when assessing cartilage thickness, further research in ultrasound beam forming may lead to improvements in replicability of cartilage echo-intensity between ultrasound units.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".