Real Time RNN Based 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip.

Acquiring adequate ultrasound (US) image data is crucial for accurate diagnosis of developmental dysplasia of the hip (DDH), the most common pediatric hip disorder affecting on average one in every one thousand births. Presently, the acquisition of high quality US deemed adequate for diagnostic measurements requires thorough knowledge of infant hip anatomy as well as extensive experience in interpreting such scans. This work aims to provide rapid assurance to the operator, automatically at the time of acquisition, that the data acquired are suitable for accurate diagnosis. To this end, we propose a deep learning model for a fully automatic scan adequacy assessment of 3D US volumes. Our contributions include developing an effective criteria that defines the features required for DDH diagnosis in an adequate 3D US volume, proposing an efficient neural network architecture composed of convolutional layers and recurrent layers for robust classification, and validating our model's agreement with classification labels from an expert radiologist on real pediatric clinical data. To the best of our knowledge, our work is the first to make use of inter-slice information within a 3D US volume for DDH scan adequacy. Using 200 3D US volumes from 25 pediatric patients, we demonstrate an accuracy of 82% with an area under receiver operating characteristic curve of 0.83 and a clinically suitable runtime of one second.