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

2025-04-08

AI Summary

Standardized diagnosis of developmental dysplasia of the hip (DDH) remains challenging, and AI research in this domain is hindered by limited access to annotated data and non-reproducible code. To address these gaps, we introduce Retuve—the first open-source, full-stack, multimodal AI framework specifically designed for DDH assessment, unifying ultrasound and radiographic image analysis. Retuve integrates deep learning–based segmentation and anatomical landmark detection models, coupled with image registration and automated geometric parameter computation, enabling end-to-end quantification of clinical metrics including the alpha angle and acetabular index. We release an expert-annotated dataset, pre-trained models, comprehensive training scripts, and a modular Python API—enhancing methodological transparency, experimental reproducibility, and clinical deployability. All code, model weights, and data are publicly available under permissive licenses, facilitating global adoption and accelerating AI-assisted early DDH screening.

Technology Category

• Computer Vision: Image and Video Retrieval
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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

Problem

• Diagnostic challenges in hip dysplasia (DDH) hinder timely intervention.
• Lack of standardized screening and reproducible AI-driven DDH studies.
• Need for open-source multi-modality DDH analysis framework (US/X-ray).

Innovation

• Open-source multi-modality DDH analysis framework
• Integrates segmentation and landmark detection models
• Provides datasets, pre-trained models, and Python API