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Objective is to assess the ability of texture features for detectingradiographic patellofemoral osteoarthritis (PFOA) from knee lateral viewradiographs. We used lateral view knee radiographs from MOST public usedatasets (n = 5507 knees). Patellar region-of-interest (ROI) was automaticallydetected using landmark detection tool (BoneFinder). Hand-crafted features,based on LocalBinary Patterns (LBP), were then extracted to describe thepatellar texture. First, a machine learning model (Gradient Boosting Machine)was trained to detect radiographic PFOA from the LBP features. Furthermore, weused end-to-end trained deep convolutional neural networks (CNNs) directly onthe texture patches for detecting the PFOA. The proposed classification modelswere eventually compared with more conventional reference models that useclinical assessments and participant characteristics such as age, sex, bodymass index(BMI), the total WOMAC score, and tibiofemoral Kellgren-Lawrence (KL)grade. Atlas-guided visual assessment of PFOA status by expert readers providedin the MOST public use datasets was used as a classification outcome for themodels. Performance of prediction models was assessed using the area under thereceiver operating characteristic curve (ROC AUC), the area under theprecision-recall (PR) curve-average precision (AP)-, and Brier score in thestratified 5-fold cross validation setting.Of the 5507 knees, 953 (17.3%) hadPFOA. AUC and AP for the strongest reference model including age, sex, BMI,WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487,respectively. Textural ROI classification using CNN significantly improved theprediction performance (ROC AUC= 0.889, AP= 0.714). We present the first studythat analyses patellar bone texture for diagnosing PFOA. Our resultsdemonstrates the potential of using texture features of patella to predictPFOA.

Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis