开源工具Retuve实现髋关节发育不良多模态自动分析，提升诊断可重复性。
Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open Source AI
- 基于超声与X光的多模态深度学习框架，集成分割与关键点检测模型。
- 提供专家标注数据集、预训练模型及完整代码，支持α角和髋臼指数自动测量。
- 开源可复现，适合医学影像研究者和临床医生用于早期筛查与科研协作。
髋关节发育不良（DDH）的诊断面临显著挑战，导致干预时机延迟。当前筛查方法缺乏标准化，而人工智能研究因数据与代码开放不足，存在可复现性问题。为此，我们提出Retuve，一个面向多模态DDH分析的开源框架，涵盖超声（US）与X线成像。Retuve提供完整的可复现工作流，包括专家标注的US与X线图像数据集、带训练代码与权重的预训练模型，以及友好的Python API。框架整合了分割与关键点检测模型，实现α角和髋臼指数等关键诊断参数的自动化测量。遵循开源原则，Retuve推动研究透明化、协作化与可及性，有望实现DDH筛查的普及化，促进早期诊断与患者预后改善。GitHub仓库地址：https://github.com/radoss-org/retuve
原文摘要 · Abstract (English)
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
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