用深度学习自动测量膝关节对线，准确度接近医生水平。
Deep Learning-based Alignment Measurement in Knee Radiographs
- 基于时钟网络和注意力门结构，自动定位百余个膝部解剖点。
- 测量误差仅约1°，术前一致性ICC达0.97，术后为0.86。
- 适合临床自动化流程，提升骨科影像分析效率。
膝关节对线（KA）测量对预测关节健康及全膝置换术后效果至关重要。传统方法依赖人工操作，耗时且需长腿片。本研究提出一种基于深度学习的方法，通过自动定位膝部解剖标志，在前后位膝关节X光片中实现对线测量。该方法基于时钟网络并引入注意力门结构，增强鲁棒性并聚焦关键解剖特征。据我们所知，这是首个能定位100多个膝部解剖点以完整勾勒膝关节形态，并同时在术前与术后图像上集成对线测量的深度学习方法。采用解剖胫股角进行测量，与临床金标准相比，平均绝对差异约为1°。术前自动化与临床测量的一致性极佳（组内相关系数ICC=0.97），术后良好（ICC=0.86）。结果表明，该方法可高精度自动化评估膝关节对线，为数字化临床工作流创造新可能。
原文摘要 · Abstract (English)
Radiographic knee alignment (KA) measurement is important for predicting joint health and surgical outcomes after total knee replacement. Traditional methods for KA measurements are manual, time-consuming and require long-leg radiographs. This study proposes a deep learning-based method to measure KA in anteroposterior knee radiographs via automatically localized knee anatomical landmarks. Our method builds on hourglass networks and incorporates an attention gate structure to enhance robustness and focus on key anatomical features. To our knowledge, this is the first deep learning-based method to localize over 100 knee anatomical landmarks to fully outline the knee shape while integrating KA measurements on both pre-operative and post-operative images. It provides highly accurate and reliable anatomical varus/valgus KA measurements using the anatomical tibiofemoral angle, achieving mean absolute differences ~1° when compared to clinical ground truth measurements. Agreement between automated and clinical measurements was excellent pre-operatively (intra-class correlation coefficient (ICC) = 0.97) and good post-operatively (ICC = 0.86). Our findings demonstrate that KA assessment can be automated with high accuracy, creating opportunities for digitally enhanced clinical workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。