Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

被引:8
|
作者
Wang, Yiqin [1 ,2 ,3 ]
Wen, Qiong [1 ,2 ,3 ]
Jin, Luhua [1 ,2 ,3 ]
Chen, Wei [1 ,2 ,3 ]
机构
[1] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Nephrol, Guangzhou 510080, Peoples R China
[2] Sun Yat Sen Univ, NHC Key Lab Clin Nephrol, Guangzhou 510080, Peoples R China
[3] Guangdong Prov Key Lab Nephrol, Guangzhou 510080, Peoples R China
基金
中国国家自然科学基金;
关键词
renal pathology; digital imaging; image interpretation; machine learning; artificial intelligence; kidney diseases; IGA NEPHROPATHY; DIGITAL PATHOLOGY; LUPUS NEPHRITIS; IMAGE-ANALYSIS; OXFORD CLASSIFICATION; HISTOLOGICAL IMAGES; LEARNING-MODEL; SEGMENTATION; CHALLENGES; FIBROSIS;
D O I
10.3390/jcm11164918
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
引用
收藏
页数:21
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