  In this study, we propose a novel framework that utilizes deep learning (DL)
and attention mechanisms to predict the radiographic progression of
patellofemoral osteoarthritis (PFOA) over a period of seven years. This study
included subjects (1832 subjects, 3276 knees) from the baseline of the MOST
study. PF joint regions-of-interest were identified using an automated landmark
detection tool (BoneFinder) on lateral knee X-rays. An end-to-end DL method was
developed for predicting PFOA progression based on imaging data in a 5-fold
cross-validation setting. A set of baselines based on known risk factors were
developed and analyzed using gradient boosting machine (GBM). Risk factors
included age, sex, BMI and WOMAC score, and the radiographic osteoarthritis
stage of the tibiofemoral joint (KL score). Finally, we trained an ensemble
model using both imaging and clinical data. Among the individual models, the
performance of our deep convolutional neural network attention model achieved
the best performance with an AUC of 0.856 and AP of 0.431; slightly
outperforming 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). The
inclusion of imaging data and clinical variables in an ensemble model allowed
statistically more powerful prediction of PFOA progression (AUC = 0.865,
AP=0.447), although the clinical significance of this minor performance gain
remains unknown. This study demonstrated the potential of machine learning
models to predict the progression of PFOA using imaging and clinical variables.
These models could be used to identify patients who are at high risk of
progression and prioritize them for new treatments. However, even though the
accuracy of the models were excellent in this study using the MOST dataset,
they should be still validated using external patient cohorts in the future.
