A Study Toward Automatic Identification of Renal Stone Composition in Single-energy or Ultra-low-dose CT Scan Using Deep Neural Networks

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.

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.

A total of 146 pure renal stone samples were first surgically collected from the patients. The stones were then inserted into a Rando phantom and scanned using a DECT scanner. An ultra-low-dose CT scan was acquired to determine the stone position prior to the DECT scan. The result of chemical analysis was used as the gold standard for determining the stone composition throughout the study. Several neural networks, including ResNet-50, ResNet-18, GoogLeNet, and VGG-19, were used to classify the stone images into three composition groups, including uric acid, calcium oxalate, and cystine. Moreover, the Taguchi method was employed to optimize the network hyperparameters. The signal-to-noise ratio (SNR) was also analyzed to determine the optimal arrangement.

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.

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 ismorewidely accessibleand safer in terms of radiation exposure.