Reliability and concurrent validity of three-dimensional ultrasound for quantifying knee cartilage volume
Bibliographic record
Abstract
The goal of this study was to test the reliability and validity of a handheld mechanical three-dimensional (3D) ultrasound (US) device for quantifying femoral articular cartilage (FAC) against the current clinical standard of magnetic resonance imaging (MRI). Bilateral knee images of 25 healthy volunteers were acquired with 3D US and 3.0 T MRI. The trochlear FAC was segmented by two raters who repeated segmentations on five cases during separate sessions. MRI and 3D US segmentations were registered using a semi-automated surface-based registration algorithm, and MRI segmentations were trimmed to match the FAC region from 3D US. Intra- (n = 5) and inter-rater (n = 25) reliabilities were assessed using intraclass correlation coefficients (ICCs) calculated from FAC volumes. Relationships between MRI and 3D US were assessed using Spearman correlation and linear regression (n = 25). MRI intra-rater ICCs were 0.97 (0.79, 1.00) and 0.90 (0.25, 0.99) for each rater with an inter-rater ICC of 0.83 (0.48, 0.94). 3D US intra-rater ICCs were 1.00 (0.98, 1.00) and 0.98 (0.84, 1.00) for each rater with an inter-rater ICC of 0.96 (0.90, 0.98). Spearman correlation and linear regression revealed a strong correlation ρ = 0.884 (0.746, 0.949) and regression R2 = 0.848 (0.750, 0.950). These results suggest 3D US demonstrates excellent intra- and inter-rater reliabilities and strong concurrent validity with MRI when quantifying healthy trochlear FAC volume. 3D US may reduce imaging costs and greatly improve feasibility of quantifying knee cartilage volume during knee arthritis clinical trials and patient care.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".