Toward automatic diagnosis of hip dysplasia from 2D ultrasound
Bibliographic record
Abstract
Developmental dysplasia of the hip (DDH) is a congenital deformity occurring in ∼3% of infants. If diagnosed early most cases of DDH can be effectively treated using a Pavlik harness. However, current diagnosis of DDH using 2D ultrasound is and can have high inter-operator variability. In this paper we propose a method to automatically segment the acetabulum bone and derive geometric indices of hip dysplasia from this model. In the proposed method, using multi-scale superpixels, we incorporate global and local image features into a Deep Learning framework to obtain a probability map of the bone to be segmented and then use this map in probabilistic graph search to guide the segmentation. Clinically relevant geometric measures of hip dysplasia, including a new index of acetabular rounding, are then automatically calculated from the segmented acetabulum contour. We tested this method on 2D ultrasound of 50 infant hips and the contours generated matched closely with manual segmentations at root mean square error 1.8±0.7 mm and Hausdorff distance 2.1±0.9 mm. In this pilot data, the measured indices of dysplasia give an area under the curve of 86.2% for classifying normal vs dysplastic hips. The proposed approach could be used clinically for accurate and automatic diagnosis of hip dysplasia in infants.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".