Infant hip screening using multi-class ultrasound scan segmentation

TL;DR

This paper introduces a deep learning-based method for automating the segmentation of ultrasound images to calculate Femoral Head Coverage, aiding in infant DDH screening with high accuracy, outperforming existing methods.

Contribution

The study presents the first automated approach for FHC calculation in DDH screening using deep learning, improving diagnostic accuracy over previous techniques.

Findings

Automated FHC calculation achieves 89.8% agreement with clinicians.

The method outperforms existing state-of-the-art segmentation techniques.

First application of deep learning for FHC in infant hip ultrasound analysis.

Abstract

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.