Assessing radial shock wave therapy in knee osteoarthritis using a morphology-based graph-cut algorithm on CT images
Bibliographic record
Abstract
This study aimed to investigate the application effect of a morphology-based automatic graph-cut algorithm for computed tomography (CT) images in assessing the efficacy of radial shockwave therapy (RSWT) in knee osteoarthritis (KOA). The study designed an automatic graph-cut algorithm based on morphological markers and compared its performance with traditional graph-cut and threshold-based graph-cut algorithms. Subsequently, 86 patients with KOA were randomly assigned to two groups: experimental group (N = 43) receiving RSWT, and control group (N = 43) receiving oral non-steroidal anti-inflammatory drugs (celecoxib capsules) as a positive control. The proposed algorithm was utilized to process knee CT images of patients before and after treatment, quantitatively analyze changes in femoral cartilage thickness, and compare Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Activity of Daily Living (AD) scores between the two groups. The proposed morphological automatic graph-cut algorithm demonstrated a processing time of (0.087 ± 0.011) s and achieved a Dice similarity coefficient of (0.945 ± 0.021), with its performance being significantly superior to the comparative algorithms ( P < 0.05). Clinical results indicated that cartilage thickness in all femoral regions increased in both groups after treatment compared to pre-treatment levels ( P < 0.05), with the experimental group exhibiting a significantly greater increase (femoral trochlea: 1.79 ± 0.22 mm vs. 2.31 ± 0.25 mm) than the control group ( P < 0.05). The experimental group showed significantly greater improvements than the control group in VAS scores (3.21 ± 0.21 vs. 4.03 ± 0.78), total WOMAC scores (39.66 ± 5.18 vs. 46.78 ± 3.95), and AD scores (25.55 ± 1.54 vs. 29.23 ± 3.22) ( P < 0.05). The automatic graph-cut algorithm based on morphological markers can efficiently and accurately segment CT images of patients with KOA, providing a reliable tool for the quantitative analysis of joint structures. Preliminary clinical results suggest that radial shockwave therapy (RSWT) may be superior to non-steroidal anti-inflammatory drug treatment in alleviating KOA pain, improving joint function, and promoting functional rehabilitation.
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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 | 0.000 |
| 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) | 0.000 | 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".