Developmental Hip Dysplasia Diagnosis at Three-dimensional US: A Multicenter Study
Bibliographic record
Abstract
Purpose To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US). Materials and Methods In this retrospective multi-institutional study, 3D US was added to conventional two-dimensional (2D) US of 1728 infants (mean age, 67 days; age range, 3-238 days) evaluated for DDH from January 2013 to December 2016. Clinical diagnosis after more than 6 months follow-up was normal (n = 1347), borderline (Graf IIa, later normalizing spontaneously; n = 140) or dysplastic (Graf IIb or higher, n = 241). Custom software accessible through the institution's research portal automatically calculated indexes including 3D posterior and anterior alpha angle and osculating circle radius from hip surface models generated with less than 1 minute of user input. Logistic regression predicted clinical diagnosis (normal = 0, dysplastic = 1) from 3D indexes (ie, age and sex). Output represented probability of hip dysplasia from 0 to 1 (output: >0.9, dysplastic; 0.11-0.89, borderline; <0.1, normal). Software can be accessed through the research portal. Results Area under the receiver operating characteristic curve was equivalently high for 3D US indexes and 2D US alpha angle (0.996 vs 0.987). Three-dimensional US helped to correctly categorize 97.5% (235 of 241) dysplastic and 99.4% (1339 of 1347) normal hips. No dysplastic hips were categorized as normal. Correct diagnosis was provided at initial 3D US scan in 69.3% (97 of 140) of the studies diagnosed as borderline at initial 2D US scans. Conclusion Automatically calculated 3D indexes of acetabular shape performed equivalently to high-quality 2D US scans at tertiary medical centers to help diagnose DDH. Three-dimensional US reduced the number of borderline studies requiring follow-up imaging by over two-thirds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".