Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images

Abstract

Introduction Developmental dysplasia of the hip (DDH) is a cluster of hip development disorders and one of the most common hip diseases in infants. Hip radiography is a convenient diagnostic tool for DDH, but its diagnostic accuracy is dependent on the interpreter’s level of experience. The aim of this study was to develop a deep learning model for detecting DDH using YOLOv5. Methods Patients younger than 12 months who underwent hip radiography between June 2009 and November 2021 were selected. Using their radiography images, transfer learning was performed to develop a deep learning model using YOLOv5. Results A total of 305 anteroposterior hip radiography images (205 normal hip images and 100 DDH hip images) were collected. Of these, 30 normal hip images and 17 DDH hip images were used as the test set. The sensitivity and the specificity of our best deep learning model (YOLOv5l) were 0.94(95%CI 0.73-1.00) and 0.96 (95%CI:0.89-0.99), respectively. Conclusion This is the first study to establish a model for detecting DDH using YOLOv5. Our deep learning models provided good diagnostic performance for DDH. We believe our model is a useful diagnostic assistant tool.