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In this study, we propose a novel framework that utilizes deep learning (DL)and attention mechanisms to predict the radiographic progression ofpatellofemoral osteoarthritis (PFOA) over a period of seven years. This studyincluded subjects (1832 subjects, 3276 knees) from the baseline of the MOSTstudy. PF joint regions-of-interest were identified using an automated landmarkdetection tool (BoneFinder) on lateral knee X-rays. An end-to-end DL method wasdeveloped for predicting PFOA progression based on imaging data in a 5-foldcross-validation setting. A set of baselines based on known risk factors weredeveloped and analyzed using gradient boosting machine (GBM). Risk factorsincluded age, sex, BMI and WOMAC score, and the radiographic osteoarthritisstage of the tibiofemoral joint (KL score). Finally, we trained an ensemblemodel using both imaging and clinical data. Among the individual models, theperformance of our deep convolutional neural network attention model achievedthe best performance with an AUC of 0.856 and AP of 0.431; slightlyoutperforming the deep learning approach without attention (AUC=0.832, AP= 0.4)and the best performing reference GBM model (AUC=0.767, AP= 0.334). Theinclusion of imaging data and clinical variables in an ensemble model allowedstatistically more powerful prediction of PFOA progression (AUC = 0.865,AP=0.447), although the clinical significance of this minor performance gainremains unknown. This study demonstrated the potential of machine learningmodels to predict the progression of PFOA using imaging and clinical variables.These models could be used to identify patients who are at high risk ofprogression and prioritize them for new treatments. However, even though theaccuracy of the models were excellent in this study using the MOST dataset,they should be still validated using external patient cohorts in the future.

Deep Learning for Predicting Progression of Patellofemoral  Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and  Symptomatic Assessments