#Normal hip xray teen series

#Normal hip xray teen series

The YOLO series is particularly renowned for its flexible structure, and many researchers have conducted object detection using improved algorithms based on the YOLO algorithm. The one-stage methods can complete both classification and position detection tasks simultaneously, and are therefore popular because of their speed and accuracy 17, 18. The one-stage object detection methods mainly used are the YOLO series and single shot multi-box detector (SSD), while the faster R-CNN is a widely adopted two-stage object detection method 15, 16. Object detection methods include one- and two-stage approaches. In this study, the “You Only Look Once” v5 (YOLOv5) method, which is one of the most widely used object detection methods, was used 14. To our knowledge, studies that have applied deep learning techniques to detect DDH using hip radiography images are limited 12, 13. Recent studies have shown that deep learning techniques can also be applied to radiographic images 7, 8, 9, 10, 11. A tool that can help inexperienced doctors diagnose DDH may be necessary and can reduce the percentage of late diagnoses.ĭeep learning technology has rapidly progressed and is widely used to detect or classify objects on images in many fields, such as face recognition. DDH is a relatively rare disease in fact, a Japanese study revealed that 32% of orthopedic doctors were yet to encounter a patient with DDH in their career as at the time of the study 1. However, the diagnostic accuracy of hip radiography for DDH varies according to the interpreter’s experience 6. Hip radiography is a convenient diagnostic tool since it is available in most hospitals and clinics. Therefore, it may be difficult to popularize the hip ultrasonography technique for physicians who conduct hip screening in infants. Hip ultrasonography is an accurate modality, but it requires a certain level of experience to achieve acceptable performance 5. Hip radiography and ultrasonography are widely used screening tools for diagnosing DDH. In our previous study, we reported that the rate of late diagnosis (diagnosed ≥ 1 year) was 10–12% in Japan, which is concerning 1. However, treatment outcomes in patients diagnosed with DDH dislocation at the age of ≥ 1 year vary, suggesting that early detection and treatment are essential for good outcomes. 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.