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

Abstract

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. 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 the “You Only Look Once” v5 (YOLOv5) and single shot multi-box detector (SSD). A total of 305 anteroposterior hip radiography images (205 normal and 100 DDH hip images) were collected. Of these, 30 normal and 17 DDH hip images were used as the test dataset. The sensitivity and the specificity of our best YOLOv5 model (YOLOv5l) were 0.94 (95% confidence interval [CI] 0.73–1.00) and 0.96 (95% CI 0.89–0.99), respectively. This model also outperformed the SSD model. This is the first study to establish a model for detecting DDH using YOLOv5. Our deep learning model provides good diagnostic performance for DDH. We believe our model is a useful diagnostic assistant tool.

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach

“…Likewise, there are several applications of numerical and deep-learning approaches to solve problems in interdisciplinary areas such as physics [31], hyperphysical [32] and healthcare sectors [33][34][35].…”

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

“…Compared with clinician-led diagnosis, the deep learning system was highly consistent, more convenient, and effective in DDH diagnosis. On top of that, Hiroki et al [79] further improved the model by using YOLOv5 for DDH detection for the first time, developing a deep learning model in combination with a single-shot multi-box detector (SSD), and introducing migration learning. The dataset contains 205 standard images and 100 DDH images.…”

Automatic and human level Graf's type identification for detecting developmental dysplasia of the hip

“…The physical assessment of DDH with Palmén and Barlow's test [ 9 , 10 ], although fast and straightforward, lacks consistent diagnostic criteria, and the results may have observer's disagreement [ 11 ] and misdiagnosis [ 11 , 12 ]. Radiography, such as X-ray imaging, can achieve high accuracy (>90%) [ [13] , [14] , [15] , [16] ] and is harmless; however, parents have raised concerns regarding exposing infants to ionized radiations [ 17 , 18 ]. Additionally, unlike ultrasound examination, X-ray imaging cannot be applied to patients under 6 months of age.…”