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The aim is to assess the effectiveness of geometric features from hip joint space in distinguishing between normal and dysplastic hips in infants.

May 4, 2026Applied SciencesOpen Access

Geometric Radiomic Analysis of Hip Joint Space for Automatic Detection of Developmental Dysplasia of the Hip in Infants

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The aim is to assess the effectiveness of geometric features from hip joint space in distinguishing between normal and dysplastic hips in infants.
Analyzed pelvic X-ray images from infants averaging 4.5 months old.
Developed custom segmentation masks to isolate the hip joint space.
Extracted 99 geometric and radiomic features and utilized supervised machine learning methods for classification.
Classification models achieved an F1-score of approximately 80% on the full dataset.
Age-matched analysis improved performance, yielding 94% accuracy and 93% recall.
Geometric characterization showed strong discriminative power between normal and DDH, p < 0.001.

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Randomized trial investigates geometric features for detecting DDH in infants, indicating improved accuracy using age-matched analysis.

Sitsiani et al. (2026) studied this question.

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