Deep learning model for automated kidney stone detection using coronal CT images

Kidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.

Automated summary

In this study, an automated kidney stone detection method using deep learning technology achieved an accuracy of 96.82% in identifying kidney stones, even in small sizes. The use of computer-aided diagnosis systems as auxiliary tools in diagnosis was demonstrated, showing promise for clinical applications. Additionally, the study highlighted the potential of employing popular deep learning methods in addressing challenging issues in urology.