ArticleDiagnostics (Basel, Switzerland)2022
Development of a Fully Automated Graf Standard Plane and Angle Evaluation Method for Infant Hip Ultrasound Scans.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 24 citations in OpenAlex.
- Artificial Intelligence-Assisted Wireless Handheld Ultrasound for Screening Developmental Dysplasia of the Hip in Infants.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- DDH-Net: A Graf-Compliant Method for Computer-Aided Assessment Using Infant Hip Ultrasound.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable Quality Assessment and Measurement from Real-World Hip Ultrasound Cine Sweeps.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence to support ultrasound in the detection of developmental dysplasia of the hip : a scoping review.Bone & joint open · 2026Article
- A Deep Learning-Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles.Journal of imaging informatics in medicine · 2026Article
- Evaluation of Operator Variability and Validation of an AI-Assisted α-Angle Measurement System for DDH Using a Phantom Model.Bioengineering (Basel, Switzerland) · 2025Article
- Current and Emerging Applications of Artificial Intelligence in Medical Imaging for Paediatric Hip Disorders-A Scoping Review.Children (Basel, Switzerland) · 2025Review
- Diagnosis on Ultrasound Images for Developmental Dysplasia of the Hip with a Deep Learning-Based Model Focusing on Signal Heterogeneity in the Bone Region.Diagnostics (Basel, Switzerland) · 2025Article
- Real-Time Ultrasound Diagnosis of Developmental Dysplasia of the Hip Using an Attention-Enhanced YOLOv11 Model.International journal of medical sciences · 2025Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Comparison Static and Dynamic Ultrasound Techniques of DDH: The Role of the Patient's Position.The archives of bone and joint surgery · 2024Article
- Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network.Joint diseases and related surgery · 2023Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundGraf's method is currently the most commonly used ultrasound-based technique for the diagnosis of developmental dysplasia of the hip (DDH). However, the efficiency and accuracy of diagnosis are highly affected by the sonographers' qualification and the time and effort expended, which has a significant intra- and inter-observer variability.
methodsAiming to minimize the manual intervention in the diagnosis process, we developed a deep learning-based computer-aided framework for the DDH diagnosis, which can perform fully automated standard plane detection and angle measurement for Graf type I and type II hips. The proposed framework is composed of three modules: an anatomical structure detection module, a standard plane scoring module, and an angle measurement module. This framework can be applied to two common clinical scenarios. The first is the static mode, measurement and classification are performed directly based on the given standard plane. The second is the dynamic mode, where a standard plane from ultrasound video is first determined, and measurement and classification are then completed. To the best of our knowledge, our proposed framework is the first CAD method that can automatically perform the entire measurement process of Graf's method.
resultsIn our experiments, 1051 US images and 289 US videos of Graf type I and type II hips were used to evaluate the performance of the proposed framework. In static mode, the mean absolute error of α, β angles are 1.71° and 2.40°, and the classification accuracy is 94.71%. In dynamic mode, the mean absolute error of α, β angles are 1.97° and 2.53°, the classification accuracy is 89.51%, and the running speed is 31 fps.
conclusionsExperimental results demonstrate that our fully automated framework can accurately perform standard plane detection and angle measurement of an infant's hip at a fast speed, showing great potential for clinical application.
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