Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty. Traditional measurement methods are manual and time-consuming, while recent machine learning approaches typically rely on locating a fixed set of anatomical landmarks. This dependence limits flexibility and may require re-annotation when clinical definitions change. To address this, we propose an automated workflow using Implicit Neural Shape Functions (INSF). Rather than relying on explicit landmark coordinates, we encode the anatomy into a compact latent space and regress clinical alignment measurements directly from these latent codes. This architecture allows for rapid extendability to new tasks without altering the backbone representation. We trained our method on an internal dataset of 566 knee radiographs, each annotated with the outline of the femur and tibia. We evaluated it on both an internal test dataset of 50 patients and a separate external set of 402 preoperative cases from the MRKR dataset. Manual clinical measurements are available for these data, and the MRKR measurements will be made publicly accessible. Performance was comparable to state-of-the-art landmark-based methods and manual agreement, while offering a flexible shape representation that can be extended to additional measurement tasks.

翻译：下肢力线（LLA）的放射学评估对于预测全膝关节置换术中的关节健康与手术效果至关重要。传统测量方法依赖人工操作且耗时较长，而近期机器学习方法通常需定位一组固定解剖标志点。这种依赖性限制了灵活性，且当临床定义发生变化时可能需重新标注。为解决此问题，我们提出一种利用隐式神经形状函数（INSF）的自动化工作流程。该方法不依赖显式标志点坐标，而是将解剖结构编码至紧凑的隐空间，并直接通过隐编码回归临床力线测量值。该架构可在不改变主干表征的前提下快速扩展至新任务。我们采用包含566张膝关节X光片的内部数据集（每张均标注股骨与胫骨轮廓）对模型进行训练，并在含50例患者的内部测试集与MRKR数据集中402例术前外部病例上开展评估。上述数据均提供人工临床测量结果，且MRKR测量数据将公开共享。本方法性能与当前最优的标志点法及人工测量水平相当，同时提供可扩展至其他测量任务的灵活形状表征。