2D Ultrasound Validation to Assess the Accuracy of Hip Displacement Measurement: A Phantom Study
Bibliographic record
Abstract
Hip displacement is a common orthopedic abnormality in children with cerebral palsy and is assessed on anteroposterior pelvic radiographs during surveillance. Repeated exposure to ionizing radiation is a major concern of cancer risks for children. Ultrasound (US) has been proposed to image the hips. The severity of hip displacement is measured by the Reimers' migration percentage (MP), which is calculated by the ratio of the femoral head distance from the acetabulum to the width of the femoral head. Methods have been published to estimate MP from the US hip images in literature; however, validation for accuracy has not been reported. This study aimed to determine the accuracy of the 2D ultrasound techniques using two 3D printed hip phantoms with known MP values. The MPs estimated from the US images were compared with those measured from the X-ray images. Based on the experimental results, the US measurements had a maximum absolute discrepancy of 2.2% as compared to 9.8% from the X-ray measurements for the MP. The study on phantoms has showed the proposed US approach is promising with better accuracy and without ionizing radiation.Clinical Relevance - If the accuracy is proved to be at least as good as the current X-ray gold standard, the proposed US method will provide a modality of choice to pediatric patients for hip displacement diagnostics and hip surveillance, especially those with cerebral palsy. The method will be free of ionizing radiation and therefore significantly improve the pediatric 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".