Current methods for predicting osteoarthritis (OA) outcomes do not incorporate disease specific prior knowledge to improve the outcome prediction models. We developed a novel approach that effectively uses consecutive imaging studies to improve OA outcome predictions by incorporating an OA severity constraint. This constraint ensures that the risk of OA for a knee should either increase or remain the same over time. DL models were trained to predict TKR within multiple time periods (1 year, 2 years, and 4 years) using knee radiographs and MRI scans. Models with and without the risk constraint were evaluated using the area under the receiver operator curve (AUROC) and the area under the precision recall curve (AUPRC) analysis. The novel RiskFORM2 method, leveraging a dual model risk constraint architecture, demonstrated superior performance, yielding an AUROC of 0.87 and AUPRC of 0.47 for 1 year TKR prediction on the OAI radiograph test set, a marked improvement over the 0.79 AUROC and 0.34 AUPRC of the baseline approach. The performance advantage extended to longer followup periods, with RiskFORM2 maintaining a high AUROC of 0.86 and AUPRC of 0.75 in predicting TKR within 4 years. Additionally, when generalizing to the external MOST radiograph test set, RiskFORM2 generalized better with an AUROC of 0.77 and AUPRC of 0.25 for 1 year predictions, which was higher than the 0.71 AUROC and 0.19 AUPRC of the baseline approach. In the MRI test sets, similar patterns emerged, with RiskFORM2 outperforming the baseline approach consistently. However, RiskFORM1 exhibited the highest AUROC of 0.86 and AUPRC of 0.72 for 4 year predictions on the OAI set.

翻译：当前预测骨关节炎（OA）结局的方法未能纳入疾病特异性先验知识以改进结局预测模型。我们开发了一种新颖方法，通过引入OA严重程度约束，有效利用连续影像学研究来改善OA结局预测。该约束确保膝关节的OA风险应随时间增加或保持不变。我们训练了深度学习模型，使用膝关节X光片和MRI扫描来预测多个时间段内（1年、2年和4年）的全膝关节置换术（TKR）。通过受试者工作特征曲线下面积（AUROC）和精确率-召回率曲线下面积（AUPRC）分析，评估了包含与不包含风险约束的模型。新颖的RiskFORM2方法利用双模型风险约束架构，展现出卓越性能，在OAI X光片测试集上对1年内TKR的预测达到了0.87的AUROC和0.47的AUPRC，相较于基线方法的0.79 AUROC和0.34 AUPRC有显著提升。这一性能优势延伸至更长的随访期，RiskFORM2在预测4年内TKR时保持了0.86的高AUROC和0.75的AUPRC。此外，当推广至外部MOST X光片测试集时，RiskFORM2泛化能力更强，对1年预测的AUROC为0.77，AUPRC为0.25，高于基线方法的0.71 AUROC和0.19 AUPRC。在MRI测试集中也出现了相似的模式，RiskFORM2始终优于基线方法。然而，在OAI数据集上，RiskFORM1对4年预测表现出最高的AUROC（0.86）和AUPRC（0.72）。