  Objective: To assess the ability of imaging-based deep learning to predict
radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view
radiographs.
  Design: Knee lateral view radiographs were extracted from The Multicenter
Osteoarthritis Study (MOST) (n = 18,436 knees). Patellar region-of-interest
(ROI) was first automatically detected, and subsequently, end-to-end deep
convolutional neural networks (CNNs) were trained and validated to detect the
status of patellofemoral OA. Patellar ROI was detected using
deep-learning-based object detection method. Manual PFOA status assessment
provided in the MOST dataset was used as a classification outcome for the CNNs.
Performance of prediction models was assessed using the area under the receiver
operating characteristic curve (ROC AUC) and the average precision (AP)
obtained from the precision-recall (PR) curve in the stratified 5-fold cross
validation setting.
  Results: Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the
reference model including age, sex, body mass index (BMI), the total Western
Ontario and McMaster Universities Arthritis Index (WOMAC) score, and
tibiofemoral Kellgren-Lawrence (KL) grade to predict PFOA were 0.806 and 0.478,
respectively. The CNN model that used only image data significantly improved
the prediction of PFOA status (ROC AUC= 0.958, AP= 0.862).
  Conclusion: We present the first machine learning based automatic PFOA
detection method. Furthermore, our deep learning based model trained on patella
region from knee lateral view radiographs performs better at predicting PFOA
than models based on patient characteristics and clinical assessments.
