  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.
