Multimodal Ultrasound Versus MRI for the Diagnosis and Monitoring of Achilles Tendinopathy: A Prospective Longitudinal Study
Bibliographic record
Abstract
Background: The diagnosis and monitoring of Achilles tendinopathy with imaging are challenging. There is a lack of studies comparing the diagnostic accuracy of magnetic resonance imaging (MRI), brightness mode ultrasound (B-mode), and power Doppler ultrasound with recent technologies such as ultrasound tissue characterization (UTC) and shear wave elastography (SWE). Purpose: To assess whether SWE and UTC, which offer quantitative values, show a superior diagnostic accuracy and capacity to detect structural improvement in Achilles tendinopathy compared with MRI, B-mode, or power Doppler. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Patients with insertional (n = 28) and midportion (n = 38) Achilles tendinopathy were evaluated at baseline and 6-month follow-up using MRI, B-mode, power Doppler, SWE, and UTC. Asymptomatic controls (n = 37) were evaluated at T 0 . Diagnostic accuracy was analyzed based on a quantitative receiver operating characteristic (ROC) analysis with quantitative cutoff values (anteroposterior diameter, Öhberg score, UTC echo type, Young modulus) and by semiquantitative Likert scale–based assessment of experienced physicians. Results: For diagnosing insertional Achilles tendinopathy, semiquantitative MRI and power Doppler were most favorable (diagnostic accuracy, 95%), while the cross-sectional area of MRI revealed 89% accuracy in the ROC analyses (area under the curve [AUC], 0.911; P < .001). For diagnosing midportion Achilles tendinopathy, semiquantitative MRI and B-mode were most favorable (diagnostic accuracy, 87%), while UTC echo types 3 and 4 revealed 86% and 87% accuracy, respectively, in the ROC analyses (AUC, 0.911 and 0.941, respectively; P < .001). However, for quantitative and semiquantitative evaluation of diagnostic accuracy in both insertional and midportion Achilles tendinopathy, there was no significant difference in favor of one imaging modality over the others. Compared with baseline, only SWE showed a significant change at the 6-month follow-up ( P = .003-.035), but there were only fair to poor monitoring accuracies of 71% (insertion) and 60% (midportion). However, compared with the other modalities, the monitoring accuracy of SWE was significantly higher ( P = .002-.039). Conclusion: There was no statistically significant difference in favor of one imaging modality over the others, but MRI revealed the highest overall diagnostic accuracy for the diagnosis of both insertional and midportion Achilles tendinopathy.
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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 | 0.000 |
| 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".