Early detection and treatment of DDH-related dislocations is highly effective, with a > 80% success rate 2, 3, 4. In our previous study, the incidence of DDH-related dislocations was 0.076% in Japan 1. DDH is one of the most common hip diseases in infants. Similar content being viewed by othersĭevelopmental dysplasia of the hip (DDH) is a cluster of hip developmental disorders, including dislocation, subluxation, and acetabular dysplasia. We believe our model is a useful diagnostic assistant tool. Our deep learning model provides good diagnostic performance for DDH. This is the first study to establish a model for detecting DDH using YOLOv5. This model also outperformed the SSD model. The sensitivity and the specificity of our best YOLOv5 model (YOLOv5l) were 0.94 (95% confidence interval 0.73–1.00) and 0.96 (95% CI 0.89–0.99), respectively. Of these, 30 normal and 17 DDH hip images were used as the test dataset. A total of 305 anteroposterior hip radiography images (205 normal and 100 DDH hip images) were collected. Using their radiography images, transfer learning was performed to develop a deep learning model using the “You Only Look Once” v5 (YOLOv5) and single shot multi-box detector (SSD). Patients younger than 12 months who underwent hip radiography between June 2009 and November 2021 were selected. The aim of this study was to develop a deep learning model for detecting DDH. Hip radiography is a convenient diagnostic tool for DDH, but its diagnostic accuracy is dependent on the interpreter’s level of experience. Developmental dysplasia of the hip (DDH) is a cluster of hip development disorders and one of the most common hip diseases in infants.
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