فهرست نشریات علمی دسترسی آزاد ایران

Background: Dual-energy computed tomography (DECT) scan is a non-invasive method for the in vivo identification of renal stone composition. However, DECT scanners have several demerits, including high cost, low accessibility, and high radiation dose to patients. Objectives: The present study aimed to investigate the efficacy of deep neural networks in the classification of renal stone types using single-energy CT imaging. The Taguchi method was used for the optimization of hyperparameters. Results: In this study, CT scans of 53 uric acid, 55 calcium oxalate, and 38 cystine stones, with diameters of 1 - 3 mm, were acquired. The deep learning findings showed that the ResNet-18 network had the highest accuracy for 120-kVp and 135-kVp CT scanning, while ResNet-50 performed better in 80-kVp CT scanning. The ResNet-50 network showed the best performance of all networks in predicting stone types in 80-kVp scanning, as indicated by its high sensitivity, specificity, and precision. Conclusion: The present results indicated that our deep learning approach could be used for the in vivo determination of renal stone types. Moreover, low-dose or ultra-low-dose single-energy CT scanning is more widely accessible and safer in terms of radiation exposure.