Abstract

BackgroundRecent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model’s accuracy.MethodsWe collected plain X-ray images of 1017 patients with a radio-opaque upper urinary tract stone. X-ray images (n = 827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model’s accuracy.ResultsUsing deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter.ConclusionCAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray.

Highlights

  • Recent increased use of medical images induces further burden of their interpretation for physicians

  • We developed a computer-aided diagnosis (CAD) algorithm with deep learning architecture to automatically detect urinary tract stones on a plain X-ray image and evaluated the efficacy of this new model

  • We divided all X-ray images into two datasets, as follows: a training dataset consisting of 827 X-ray images from Tsuchiura Kyodo General Hospital and Tokyo Medical and Dental University, which were used to develop the CAD algorithm; and a test dataset consisting of 190 X-ray images from the JA Toride Medical Center, which were used to evaluate the model that we created

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Summary

Introduction

Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, diagnosing urolithiasis using this method is not always easy. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model’s accuracy. Non-contrast computed tomography (CT) has become the gold standard modality as an imaging examination for diagnosing urolithiasis because of its high accuracy, which is reportedly 92–100%, and its excellent ability to detect other acute flank pain conditions [1,2,3]. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, its accuracy, which is reportedly 44–77%, is inferior to that of CT [9]

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