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

Tong Xu; Xian-Ya Zhang; Na Yang; Fan Jiang; Gong-Quan Chen; Xiao-Fang Pan; Yue-Xiang Peng; Xin-Wu Cui

doi:10.3389/fonc.2023.1252630

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

Front Oncol. 2024 Mar 1:13:1252630. doi: 10.3389/fonc.2023.1252630. eCollection 2023.

Authors

Tong Xu¹, Xian-Ya Zhang¹, Na Yang², Fan Jiang³, Gong-Quan Chen⁴, Xiao-Fang Pan⁵, Yue-Xiang Peng⁶, Xin-Wu Cui¹

Affiliations

¹ Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China.
³ Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China.
⁴ Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China.
⁵ Health Medical Department, Dalian Municipal Central Hospital, Dalian, China.
⁶ Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China.

Abstract

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.

Keywords: artificial intelligence; deep learning; kidney; machine learning; ultrasound.

Publication types

Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work has received funding from the High-end Foreign Experts Recruitment Plan of China (grant no. G2022154032L).