BoneAgeTW2：通过坦纳-怀特豪斯2法、深度学习以及带分布曲线的临床报告生成实现骨骼成熟度自动评估
BoneAgeTW2: Automated Skeletal Maturation Assessment via the Tanner-Whitehouse 2 Method, Deep Learning, and Clinical Report Generation with Distribution Curves
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中文总结 AI 辅助
BoneAgeTW2系统利用YOLOv8和EfficientNet - B3等技术，通过伪标签策略在公共数据集上训练，能从X光图像中自动检测定位20块TW2手部骨骼并分配成熟阶段，还能生成含分布曲线的临床报告，实现骨骼成熟度评估的自动化。
中文摘要 AI 辅助
我们展示了BoneAgeTW2，这是首个将完整的坦纳-怀特豪斯2（TW2）临床方案用于骨骼成熟度评估的全开源端到端系统。该系统使用YOLOv8从X光图像中精确检测和定位20块TW2手部骨骼，并用带有20个独立分类头的EfficientNet - B3主干同时为每块骨骼分配成熟阶段（A - I）。由此预测自动生成临床PDF报告，含20块骨骼的交互式高斯分布曲线以便与群体标准直接比较。模型用伪标签策略在公共RSNA儿科骨龄挑战数据集（12,611张手部X光片）上训练，从全局骨龄注释中得出每块骨骼的阶段标签，完整代码库可公开获取。
英文摘要
We present BoneAgeTW2, the first fully open-source system to automate the complete Tanner-Whitehouse 2 (TW2) clinical protocol for skeletal maturity assessment end-to-end. The system employs YOLOv8 for precise detection and localization of the 20 TW2 hand bones from radiographic images, and an EfficientNet-B3 backbone with 20 independent classification heads to assign maturation stages (A-I) to each bone simultaneously. From these predictions, the system automatically generates clinical PDF reports including interactive Gaussian distribution curves for all 20 bones, enabling direct comparison with population norms. The model is trained on the public RSNA Pediatric Bone Age Challenge dataset (12,611 hand radiographs) using a pseudo-labeling strategy to derive per-bone stage labels from global bone age annotations. The full codebase is publicly available at https://github.com/jmmana/BoneAgeTW2.
发表机构
- Universidad de La Salle, Bogota（拉萨尔大学，波哥大）
机构由 AI 辅助整理，请以论文原文为准。
