Characterizing Shape Variations in Infant Hips Using Statistical Shape Modeling for Ultrasonic Diagnosis of Hip Dysplasia with Graf’s Method
Bibliographic record
Abstract
Purpose: Through the measurement of Graf's alpha (α) angle, the Graf method uses two-dimensional ultrasound (US) to diagnose developmental dysplasia of the hip (DDH) in infants. However, this unidimensional index cannot fully reflect anatomic shape features and variations of iliac wing and bony acetabular roof (IW-AR) coronal outlines that may influence DDH. This study aimed to analyze the shapes of IW-AR outlines by revealing their mean shape, possible shape variations, and the impact of these variations on the α angle variability. Materials and Methods: By segmenting US images of 510 infant hips, IW-AR outlines in Graf's standard plane were obtained from a mixed screening population. A statistical shape model (SSM) was then developed to analyze the outline shapes. Results: In the IW-AR outlines, shape variations were described by linear combinations of six global and local shape modes. A global mode, dominantly causing an entire outline to bend about the vicinity of its apex, could affect the α angle in a large range (38°-70°). Although a local mode produced bending patterns that had a lesser impact on the α angle (up to 6°), it may relate to DDH diagnosis and clinical outcome. Conclusion: Shape variations in IW-AR outlines can be effectively modeled by using a compact SSM representative of the variations as linear combinations of a few global and local modes. The shape variations and the angle variability by the local modes should not be underestimated even though the effect of global modes on the α angle is dominant.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".