Lau Sing-Hin, Chan Lok-Chun, Jiang Tianshu, Zhang Jiang, Meng Xiangqiao, Wang Wei, Chan Ping-Keung, Cai Jing, Li Ping, Wen Chunyi
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.
Osteoarthr Cartil Open. 2025 Aug 20;7(4):100663. doi: 10.1016/j.ocarto.2025.100663. eCollection 2025 Dec.
We developed and validated an artificial intelligence pipeline that leverages diffusion models to enhance prognostic assessment of knee osteoarthritis (OA) by analyzing longitudinal changes in patella shape on lateral knee radiographs.
In this retrospective study of 2,913 participants from the Multicenter Osteoarthritis Study, left-knee weight-bearing lateral radiographs obtained at baseline and 60 months were analyzed. Our pipeline commences with an automatic segmentation for patella shapes, followed by a diffusion model to predict patella shape trajectories over 60 months. We developed the Synthetic Patella Shape Incorporated Convolutional Neural Network (SynPatNet), a specialized 2-channel 1-dimensional convolutional neural network (CNN), to incorporate both baseline and synthetic follow-up patella shapes for predicting key outcomes of disease onset and end-stage.
The diffusion model generates plausible synthetic patella shapes that predict deformations and osteophyte developments at the 60-month follow-up. Incorporating synthetic follow-up shapes with baseline patella shapes significantly improved OA outcome prediction: for patellofemoral OA onset, SynPatNet achieved an area under receiver operating characteristic curve (AUC) of 0.909 (vs. 0.830 for baseline model); for knee replacement, an AUC of 0.823 (vs. 0.773 for baseline). Augmenting Kellgren-Lawrence (KL) grade with SynPatNet further improved knee replacement prediction (AUC 0.838) over KL grade alone (AUC 0.785). Noteworthily, our knee replacement risk prediction score showed significant correlations with MRI-based (osteophytes/cartilage morphology/bone attrition) gradings, with Spearman's rho up to (0.51/0.33/0.31, p < 0.001).
Generative diffusion modelling of patellar morphology on lateral knee radiographs provides complementary information to conventional radiographic and clinical metrics that substantially improves prognostication of knee OA.
我们开发并验证了一种人工智能流程，该流程利用扩散模型，通过分析膝关节侧位X线片上髌骨形状的纵向变化，来加强对膝关节骨关节炎（OA）的预后评估。
在这项对多中心骨关节炎研究中2913名参与者的回顾性研究中，分析了在基线期和60个月时获得的左膝负重侧位X线片。我们的流程首先对髌骨形状进行自动分割，然后使用扩散模型预测60个月内的髌骨形状轨迹。我们开发了合成髌骨形状合并卷积神经网络（SynPatNet），这是一种专门的双通道一维卷积神经网络（CNN），用于合并基线和合成的随访髌骨形状，以预测疾病发作和终末期的关键结果。
扩散模型生成了合理的合成髌骨形状，可预测60个月随访时的变形和骨赘发展。将合成随访形状与基线髌骨形状相结合，显著改善了OA结果预测：对于髌股关节OA发作，SynPatNet的受试者工作特征曲线下面积（AUC）为0.909（基线模型为0.830）；对于膝关节置换，AUC为0.823（基线为0.773）。用SynPatNet增强Kellgren-Lawrence（KL）分级，相比于单独使用KL分级，进一步改善了膝关节置换预测（AUC 0.838）（AUC 0.785）。值得注意的是，我们的膝关节置换风险预测评分与基于MRI的（骨赘/软骨形态/骨磨损）分级显示出显著相关性，Spearman相关系数高达（0.51/0.33/0.31，p<0.001）。
膝关节侧位X线片上髌骨形态的生成性扩散建模为传统的放射学和临床指标提供了补充信息，极大地改善了膝关节OA的预后。
