Wireless Ultrasound Probes: A New Frontier In Assessing Femoral Cartilage Health
Bibliographic record
Abstract
Ultrasound (US) is a non-invasive imaging technique that can accurately assess cartilage health. Recent advancements in wireless US technology allows for point of care cartilage imaging, but validation against diagnostic units remains to be established for knee cartilage outcomes. PURPOSE: Estimate agreement of articular cartilage thickness and echo-intensity between standard and wireless US units. METHODS: Using standard and wireless US, femoral cartilage was assessed via transverse suprapatellar scans with the knee in maximum flexion in 71 female NCAA Division 1 athletes (age: 20.0 ± 1.3 years, height: 171.7 ± 8.7 cm, mass: 69.4 ± 11.0 kg). Images were captured successively in the same location removing the unit after each acquisition. The wireless US unit used an auto-gain, while the standard unit used a fixed gain. Each image was greyscale-normalized by rescaling to the most hyper- and hypoechoic pixels to account for unit settings. The global femoral cartilage was manually segmented in each image. A semi-automated program was used to quantify the mean thickness and echo-intensity of raw and normalized images for the global region of interest, as well as for the medial, middle, and lateral subregions. Intraclass correlation coefficients (ICC2,k) for absolute agreement, standard error of the measurement, and minimum detectable difference were calculated between the standard and wireless US units. RESULTS: Cartilage thickness demonstrated good to excellent agreement for all regions (ICC2,k = 0.88-0.96; Table 1). However, mean echo-intensity demonstrated poor agreement in all regions for raw (ICC2,k = 0.43-0.68) and normalized images (ICC2,k = 0.45-0.58). CONCLUSIONS: Wireless US units produce similar results for knee cartilage thickness compared to their diagnostic counterparts. However, further research is needed to understand the error involved when measuring echo-intensity using different US machines so that comparison across units is possible. Dr. Harkey was supported by a National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant (K01 AR081389). ChatGPT was used to revise the content of this research abstract
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".