Hybrid Symmetry Mamba Network for Ultrasound-Based CAD of Developmental Dysplasia of the Hip.

Developmental dysplasia of the hip (DDH) is the most common congenital joint disease in infant. The B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) can help sonologists improve diagnostic accuracy for DDH. The routine CAD models mainly developed based on convolutional neural network or Transformer, which cannot fully learn the inherent structural and texture properties in the hip BUS images. The newly proposed Mamba model has shown its superior performance for learning feature representation with linear computation complexity. However, the scanning mechanism still effect the performance of state space model in Mamba to learns sequence information. To this end, a novel Hybrid Symmetry Mamba Network (HSMN) is proposed to improve the diagnostic performance of CAD model for DDH. The HSMN conducts both the symmetry convolution operation and symmetry scanning in Mamba to more effectively extract inherent information to represent the symmetrical structure in hip BUS images for DDH. The experimental results indicate that the proposed HSMN achieves a diagnostic accuracy of 89.28±2.32%, and outperforms all the compared algorithms, suggesting its effectiveness.Clinical RelevanceThis CAD model has the potential to be applied in clinical practice for help sonologist improve diagnostic accuracy of DDH.