Acetabular-vision hip developmental dysplasia’s (AV-DDH)

The labeled dataset is organized as follows: • Training Set: 16,205 images • Validation Set: 463 images • Test Set: 926 images Content: • Images: 2417 raw X-ray images in JPG format depicting hip X-rays from infants under three years of age. • Annotations: Each image is annotated with the locations of critical anatomical features relevant to DDH, stored in both JSON and YAML formats. • Demographics: Each record includes demographic data such as age, gender, and the Acetabular Index in degrees for both the left and right hips. • Angle Measurements: The dataset includes angle measurements for the Acetabular Index for both hips, verified and labeled by a team of medical professionals.

The dataset was collected from the radiology department at the Jordan University Hospital and is enriched with diagnostic information confirmed by orthopedic specialists. The images were gathered following strict inclusion criteria and are free from duplicates, ensuring the integrity of the dataset. Use Case: The AV-DDH dataset is specifically designed to aid in the development of machine learning models, especially deep learning-based approaches, to automate the diagnosis and analysis of DDH using hip X-ray images.

It provides a high-quality, reliable source of labeled data that can be used for training, testing, and validating AI models aimed at improving the early detection and diagnosis of DDH, which is crucial for timely treatment and preventing long-term complications such as osteoarthritis and hip replacement surgery. Data Format: • Images: Raw X-ray images in JPG format. • Annotations: Annotations in JSON and YAML formats. • Additional Information: Demographic data in an Excel sheet.

Keywords: Developmental Dysplasia of the Hip (DDH), X-ray, Machine Learning, Medical Imaging, Deep Learning, Data Annotation, Acetabular Index, AI, Dataset, Hip Imaging, Radiology, Diagnosis.