Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open Source AI

TL;DR

DDH screening is hindered by nonstandardized workflows and reproducibility gaps. Retuve provides an open-source, modular framework for automated multi-modality DDH analysis (ultrasound and X-ray) with open datasets, pretrained models and a Python API, plus an AI plugin system and rule-based measurement pipelines. Validation on shared datasets shows strong X-ray measurement reliability and competitive DDH classification, while ultrasound measurements demonstrate good consistency with some calibration needs. By emphasizing transparency and community collaboration, Retuve aims to democratize DDH screening, enable multi-center benchmarking, and lay the groundwork for expanded clinical validation and broader data sharing.

Abstract

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

Figures (10)

• Figure 1: Retuve can analyse ultrasounds and X-rays to generate conventional and new indices quantifying hip anatomy, such as acetabular alpha angle and coverage (ultrasound), and acetabular index (X-ray). These indices can be used to train the next generation of AI Models to diagnose DDH.
• Figure 2: Retuve Architecture Diagram, with Inputs, AI Outputs, and final outputs for the user.
• Figure 3: Retuve can easily be adjusted with a custom AI trained on any data, including forking to closed versions by those holding private data.
• Figure 4: The ultrasound algorithm illustrated in 4 steps.
• Figure 5: The algorithm for X-ray. Triangle segmentations are used to find the inner (yellow), outer (blue), and lower (green, h point) landmarks on each side of the pelvis. This allows us to calculate the Acetabular Index, Wilberg Index and IHDI grade.