Enhancing kidney stone diagnosis with AI-driven radiographic imaging: a review

Abstract

Abstract Kidney stones are collections of crystals formed from minerals and other substances within the urinary tract. Early identification of kidney stones is important because it may help avoiding complications, enhancing treatment outcomes, and boosting overall quality of life. Identifying kidney stones involves various imaging, laboratory, and diagnostic techniques. Machine learning (ML) and deep learning (DL) techniques have demonstrated their capabilities in identifying kidney stones from radiographic imaging (i.e., CT Scan, X-ray, MRI, etc.). However, ML and DL techniques are yielding good results in not only detecting kidney stone but also their size and locations as well. Kidney stone identification and classification are often addressed in the literature using multi-modal imaging data where different modalities capture different aspects of the disorder. In recent years, various ML and DL driven solutions have been introduced to address this issue. This paper represents an in-depth evaluation of several machine learning and deep learning techniques employed for the identifying kidney stones from radiographic imaging. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we performed a systematic collection of research published in the past decade by eminent publishers. The review specifically discusses the clinical application of multi-modal imaging methods, including computed tomography, X-rays, and ultra-sounds, used in kidney stone detection and classification. It also explores the use of these techniques for recognizing other urological diseases. The methodology and performance metrics for each study are critically examined. The aim of this study is to provide insights into the current status of machine learning and deep learning applications for the identification and classification of kidney stones across different imaging modalities.

Topics & Concepts