Why the study?
Ultrasound is the primary screening method for developmental dysplasia of the hip, but its interpretation remains highly operator-dependent.
Does a supervised machine learning image segmentation model accurately recognize anatomical structures in hip ultrasound images for developmental dysplasia of the hip screening?
Does a supervised machine learning image segmentation model accurately recognize anatomical structures in hip ultrasound images for developmental dysplasia of the hip screening?
A machine learning-based image segmentation model demonstrates high accuracy in recognizing anatomical structures in hip ultrasound images, potentially enhancing the reliability of developmental dysplasia of the hip screening.
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ML segmentation achieves technical feasibility in hip US; leaves open whether it improves DDH detection or reduces operator variability in prospective use.
Background: Developmental dysplasia of the hip (DDH), if not treated, can lead to osteoarthritis and disability. Ultrasound (US) is a primary screening method for the detection of DDH, but its interpretation remains highly operator-dependent. We propose a supervised machine learning (ML) image segmentation model for the automated recognition of anatomical structures in hip US images. Methods: We conducted a retrospective observational analysis based on a dataset of 10,767 hip US images from 311 patients. All images were annotated for eight key structures according to the Graf method and split into training (75.0%), validation (9.5%), and test (15.5%) sets. Model performance was assessed using the Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). Results: The best-performing model was based on the SegNeXt architecture with an MSCAN_L backbone. The model achieved high segmentation accuracy (IoU; DSC) for chondro-osseous border (0.632; 0.774), femoral head (0.916; 0.956), labrum (0.625; 0.769), cartilaginous (0.672; 0.804), and bony roof (0.725; 0.841). The average Euclidean distance for point-based landmarks (bony rim and lower limb) was 4.8 and 4.5 pixels, respectively, and the baseline deflection angle was 1.7 degrees. Conclusions: This ML-based approach demonstrates promising accuracy and may enhance the reliability and accessibility of US-based DDH screening. Future applications could integrate real-time angle measurement and automated classification to support clinical decision-making.
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Pulik et al. (2025) studied this question.
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