PURPOSE: The aim of this research was to develop a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques applied to computed tomography images of the parotid glands and to assess its efficacy by temporal validation. MATERIALS AND METHODS: In total, 132 parotid glands from 66 subjects (33 patients with Sjögren's syndrome and 33 controls) were analyzed. Radiomics features were extracted from manually segmented parotid glands using 3D Slicer. The volume data for 108 parotid glands were chronologically assigned to the training dataset, and the features extracted were imported into Prediction One (Sony Network Communications Inc, Tokyo, Japan). A prediction model was automatically generated. The area under the curve (AUC), accuracy, precision, recall, and F-value were calculated for internal validation. Temporal validation was performed using 24 images of the parotid glands obtained later. RESULTS: A total of 129 radiomics features were extracted, including 18 first-order, 14 shape, and 75 texture features. The internal validation test showed high performance, with an AUC of 0.92, accuracy of 0.88, precision of 0.90, recall of 0.85, and an F-value of 0.88. Temporal validation testing also showed high performance, with an AUC of 0.96. accuracy of 0.88, precision of 0.85, recall of 0.92, and an F-value of 0.88. CONCLUSION: The prediction model effectively differentiated Sjögren's syndrome using radiomics and machine learning. Use of Prediction One significantly streamlined the workflow, including analysis of radiomics, creation of the prediction model, and evaluation of performance, while substantially reducing the time required.