A Comprehensive Review on Kidney Stone Detection using Machine Learning and Deep Learning Techniques

Abstract

Kidney stone disease is a common urological disease, which impacts a significant proportion of the population across the globe. It should be diagnosed early and properly because in the cases that are not properly dealt with, the complications may include obstruction of urine and permanent damage to the kidneys. Computed Tomography (CT) imaging is usually the method of choice among the existing diagnostic techniques because it is highly sensitive and can clearly display the arrangement of the stones. Nevertheless, the process of manual interpretation of medical images can be time-consuming and can be subject to variation, since the medical diagnosis can be determined by the experience of the radiologist. As computational methods developed, there has been an increase in interest in using Machine Learning (ML) and Deep Learning (DL) methods to automate the process of kidney stone detection. These techniques have demonstrated a possibility of enhancing uniformity and diagnostic capability. The review provides a summary of current approaches to kidney stones detection, including classic image processing algorithms, classical machine learning systems, and more modern deep learning models like Convolutional Neural Networks (CNNs) and object detection models like YOLO. Moreover, the paper addresses popular datasets, metrics of evaluation, and practical issues, such as limited data, the challenge of identifying stones of a small size, and the problem of model generalization in various clinical scenarios. Other recent directions, including hybrid modeling and explainable AI are also discussed, which could enhance the transparency and clinical adoption of automated systems. On the whole, this survey will equip a systematic knowledge of the existing trends and also pinpoint areas that need to be researched more in the context of automated kidney stone detection.