Automation of Clinical Measurements on Radiographs of Children's Hips

Peter Thompson, Daniel C. Perry, Timothy F. Cootes, Claudia Lindner

Abstract: Developmental dysplasia of the hip (DDH) and cerebral palsy (CP) related hip migration are two of the most common orthopaedic diseases in children, each affecting around 1–2 in 1000 children. For both of these conditions, early detection is a key factor in long term outcomes for patients. However, early signs of the disease are often missed and manual monitoring of routinely collected radiographs is time-consuming and susceptible to inconsistent measurement. We propose an automatic system for calculating acetabular index (AcI) and Reimer’s migration percentage (RMP) from paediatric hip radiographs. The system applies Random Forest regression-voting to fully automatically locate the landmark points necessary for the calculation of the clinical metrics. We show that the fully automatically obtained AcI and RMP measurements are in agreement with manual measurements obtained by clinical experts, and have replicated these findings in a clinical dataset. Such a system allows for the reliable and consistent monitoring of DDH and CP patients, aiming to improve patient outcomes through hip surveillance programmes.