Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

Abstract

The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cutoff values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination. Keywords Computed tomography • Ureteral calculi • Pelvic phlebolith • Deep learning • Convolutional neural networks Abbreviations NECT Non-contrast-enhanced computed tomography CTU CT urography CNN Convolutional neural network 2.5D-CNN 2.5-Dimensional CNN including axial, coronal and sagittal images CAD Computer-aided diagnosis ANN Artificial neural network AI Artificial intelligence FC layer Fully connected layer PACS Picture archiving and communication system MPR Multiplanar reconstructions CI Confidence interval ROC Receiver operating characteristic AUC Area under the curve ROI Region of interest Key points A Convolutional Neural Network for classifying pelvic calcifications was developed and validated.

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