Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review

Abstract

Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.

Keywords: Artificial intelligence; Chronic kidney disease; Deep learning; Radiomics.

Graphical abstract

Fig. 1

Risk of bias assessments of selected publications through the modified QUADAS-2. QUADAS-2: Quality Assessment of Diagnostic Accuracy Studies-2.

Fig. 2

Flowchart of radiomics and deep learning. (A). Basic steps in radiomics, including medical images acquisition, regions of interest (ROIs), features extraction, features selection and classifier construction. (B) Deep learning directly generates deep neural networks (f) after medical image acquisition, replacing steps b-e in radiomics in Figure A, thus enabling end-to-end learning.