Background: Developmental dysplasia of the hip (DDH), if not treated, can lead to os-teoarthritis and disability. Ultrasound (US) is a primary screening method for the de-tection 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: A dataset of 10, 767 images from 311 patients was 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 ar-chitecture with an MSCANL backbone. The model achieved high segmentation accu-racy, (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 av-erage 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. Conclu-sions: This ML-based approach demonstrates promising accuracy and may enhance the reliability and accessibility of US-based DDH screening. Future applications could in-tegrate real-time angle measurement and automated classification to support clinical decision-making.
Pulik et al. (Mon,) studied this question.
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