Objective:To investigate the diagnostic value of machine learning model based on 18F-FDG PET/CT for polymyalgia rheumatica (PMR). Methods:From November 2014 to December 2022, 177 patients (119 males, 58 females; age: 67.0 ( 61.0, 72.0) years) admitted to the Department of Rheumatology and Immunology, the First People′s Hospital of Changzhou, with suspected PMR and undergoing 18F-FDG PET/CT examination were retrospectively analyzed. Patients were randomly divided into training set and validation set at the ratio of 7∶3. Three machine learning models, including classification and regression tree (CART), the least absolute shrinkage and selection operator (LASSO) algorithm, and logistic regression, were established based on the PET/CT imaging features to aid in the diagnosis of PMR. The diagnostic efficacy of each model was evaluated by ROC curve analysis and differences among AUCs were analyzed by Delong test. Results:There were 78(44.1%, 78/177) PMR patients and 99(55.9%, 99/177) non-PMR patients, and 124 patients in the training set and 53 patients in the validation set. The logistic regression model (training set: AUC=0.961; validation set: AUC=0.930) was superior to the CART (training set: AUC=0.902, z=2.96, P=0.003; validation set: AUC=0.844, z=2.46, P=0.014) in diagnosing PMR, and was similar to LASSO algorithm (training set: AUC=0.957, z=0.95, P=0.340; validation set: AUC=0.930, z=0.00, P=1.000), but with fewer sites evaluated. The simplified PMR-Logit score had the AUC of 0.951 in the overall population, with the sensitivity of 89.74%(70/78) and the specificity of 90.91%(90/99). Conclusion:Machine learning models based on 18F-FDG PET/CT imaging features are expected to be an effective diagnostic tool for PMR.