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Background: Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center-edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies., Methods: This study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments., Results: The U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05)., Conclusion: Utilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH., (© 2024. The Author(s).)

Periacetabular osteotomy (PAO) is a surgical procedure to correct acetabular orientation in developmental dysplasia of the hip (DDH). In this work, magnetic resonance imaging-based finite element models of hip joint were introduced to perform virtual surgery planning for patients with DDH. The optimal position of acetabulum following PAO was determined by analyzing the peak contact pressure and contact area between the femoral and pelvic cartilage when the acetabulum rotated from the original center edge (CE) angle in increments of 5°. Furthermore, with finite element analysis, we simulated joint movements in both the preoperative model and the planned model with the optimal position for the six cases. A comparison between the preoperative and planned models for all scenarios was made. The outcomes of finite element analysis revealed that the optimal position of acetabulum was at a CE angle of 20°. In all evaluated scenarios, the planned model showed lower peak contact pressure and larger contact area than the preoperative model did. The efficacy of virtual surgery planning was thus validated from a biomechanical perspective.