Infant hip screening using multi-class ultrasound scan segmentation
This addresses infant hip screening by automating a diagnostic process, though it is incremental as it builds on existing segmentation methods for a specific medical application.
The researchers tackled the problem of automating screening for developmental dysplasia of the hip (DDH) in infants by developing a deep learning algorithm that segments ultrasound images to calculate Femoral Head Coverage (FHC), achieving 89.8% agreement with expert clinicians.
Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images.