Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open Source AI
This work addresses diagnostic challenges in DDH screening for medical professionals, offering a reproducible and accessible tool, though it is incremental as it builds on existing AI methods with open-source improvements.
The paper tackles the problem of diagnosing developmental dysplasia of the hip (DDH) by introducing Retuve, an open-source framework for multi-modality analysis using ultrasound and X-ray imaging, which provides automated measurement of diagnostic parameters like the alpha angle and acetabular index.
Developmental dysplasia of the hip (DDH) poses significant diagnostic challenges, hindering timely intervention. Current screening methodologies lack standardization, and AI-driven studies suffer from reproducibility issues due to limited data and code availability. To address these limitations, we introduce Retuve, an open-source framework for multi-modality DDH analysis, encompassing both ultrasound (US) and X-ray imaging. Retuve provides a complete and reproducible workflow, offering open datasets comprising expert-annotated US and X-ray images, pre-trained models with training code and weights, and a user-friendly Python Application Programming Interface (API). The framework integrates segmentation and landmark detection models, enabling automated measurement of key diagnostic parameters such as the alpha angle and acetabular index. By adhering to open-source principles, Retuve promotes transparency, collaboration, and accessibility in DDH research. This initiative has the potential to democratize DDH screening, facilitate early diagnosis, and ultimately improve patient outcomes by enabling widespread screening and early intervention. The GitHub repository/code can be found here: https://github.com/radoss-org/retuve