A narrative review on the application of artificial intelligence in renal ultrasound.

Journal: Frontiers in oncology

Volume: 13

Issue: 

Year of Publication: 

Affiliated Institutions:  Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Department of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China. Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China. Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China. Health Medical Department, Dalian Municipal Central Hospital, Dalian, China. Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China.

Abstract summary 

Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.

Authors & Co-authors:  Xu Zhang Yang Jiang Chen Pan Peng Cui

Study Outcome 

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Statistics
Citations :  Lv JC, Zhang LX. Prevalence and disease burden of chronic kidney disease. Adv Exp Med Biol (2019) 1165:3–15. doi: 10.1007/978-981-13-8871-2_1
Authors :  8
Identifiers
Doi : 1252630
SSN : 2234-943X
Study Population
Male,Female
Mesh Terms
Other Terms
artificial intelligence;deep learning;kidney;machine learning;ultrasound
Study Design
Study Approach
Country of Study
Publication Country
Switzerland