ArticleScientific reports2023
Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Artificial Intelligence to Detect Developmental Dysplasia of Hip: A Systematic Review.Journal of paediatrics and child health · 2025Pooled it
- The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2024Pooled it
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- A method for early ultrasound classification of developmental dysplasia of the hip in infants: combining YOLOv8-based keypoint detection with radiomics.BMC pediatrics · 2026Article
- Scaling Early Detection for Developmental Dysplasia of the Hip With Artificial Intelligence-Assisted Imaging.Cureus · 2026Review
- A Computer Vision and Machine Learning Approach to Classify Views in Distal Radius Radiographs.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Evaluation of Operator Variability and Validation of an AI-Assisted α-Angle Measurement System for DDH Using a Phantom Model.Bioengineering (Basel, Switzerland) · 2025Article
- A dataset for quality evaluation of pelvic X-ray and diagnosis of developmental dysplasia of the hip.Scientific data · 2025Article
- Current and Emerging Applications of Artificial Intelligence in Medical Imaging for Paediatric Hip Disorders-A Scoping Review.Children (Basel, Switzerland) · 2025Review
- Overcoming barriers in the use of artificial intelligence in point of care ultrasound.NPJ digital medicine · 2025Review
- Automated Image Clarity Detection for the Improvement of Colposcopy Imaging with Multiple Devices.Biomedical signal processing and control · 2025Article
- Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip.IEEE journal of translational engineering in health and medicine · 2025Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements.Scientific reports · 2024Article
- Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information.Journal of imaging informatics in medicine · 2024Article
- Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach.Journal of imaging · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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