Fully Automating Graf's Method for DDH Diagnosis Using Deep Convolutional Neural Networks

Developmental dysplasia of the hip (DDH) is a condition affecting up to 1 in 30 infants. DDH is easy to treat if diagnosed early, but undiagnosed DDH can result in life-long hip pain, dysfunction and an increased risk of early onset osteoarthritis, and accounts for around 30 % of all hip replacements in patients under 60. The gold standard for diagnosis in infants is an ultrasound scan, followed by an analysis procedure known as Graf’s method. The application of Graf’s method is notoriously operator-dependent, requiring years of training to reach reasonable and reproducible performance. We describe a novel deep-learning based pipeline that applies Graf’s method to ultrasound scans of the hip. We use a convolutional network with an adversarial component to segment the image into relevant landmarks, and define a set of post-processing rules to translate the segmentations into Graf’s metrics. Comparing our pipeline to estimates made by experts in DDH diagnosis shows promising results.