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

Hiroki Den; Junichi Ito; Akatsuki Kokaze

doi:10.1038/s41598-023-33860-2

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

Sci Rep. 2023 Apr 24;13(1):6693. doi: 10.1038/s41598-023-33860-2.

Authors

Hiroki Den^{1

2}, Junichi Ito³, Akatsuki Kokaze⁴

Affiliations

¹ Department of Orthopaedic Surgery, National Rehabilitation Center for Children with Disabilities, 1-1-10 Komone, Itabashi-ku, Tokyo, 173-0037, Japan. denh@med.showa-u.ac.jp.
² Department of Hygiene, Public Health, and Preventative Medicine, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8555, Japan. denh@med.showa-u.ac.jp.
³ Department of Orthopaedic Surgery, National Rehabilitation Center for Children with Disabilities, 1-1-10 Komone, Itabashi-ku, Tokyo, 173-0037, Japan.
⁴ Department of Hygiene, Public Health, and Preventative Medicine, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8555, Japan.

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.

MeSH terms

Deep Learning*
Developmental Dysplasia of the Hip*
Hip Dislocation, Congenital* / diagnosis
Humans
Infant
Radiography