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

Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral  View Radiographs Using Deep Learning: Data from the Multicenter  Osteoarthritis Study (MOST)