Zhao Dan, Wang Wei, Tang Tian, Zhang Ying-Ying, Yu Chen
Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China.
Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China.
Comput Struct Biotechnol J. 2023 May 30;21:3315-3326. doi: 10.1016/j.csbj.2023.05.029. eCollection 2023.
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.
慢性肾脏病（CKD）会对肾脏结构和功能造成不可逆的损害。CKD由多种病因引起，其危险因素包括高血压和糖尿病。随着全球患病率的逐渐上升，CKD已成为全球重要的公共卫生问题。医学成像通过非侵入性识别宏观肾脏结构异常，已成为CKD的重要诊断工具。人工智能（AI）辅助医学成像技术可帮助临床医生分析肉眼难以辨别的特征，为CKD的识别和管理提供有价值的信息。最近的研究表明，基于放射组学和深度学习的AI算法的AI辅助医学图像分析作为一种临床支持工具，可有效改善各种形式CKD（包括常染色体显性多囊肾病）的早期检测、病理评估和预后评估。在此，我们概述了AI辅助医学图像分析在CKD诊断和管理中的潜在作用。
