Research progress of artificial intelligence in predicting the composition of kidney stones
10.3760/cma.j.cn112330-20230607-00200
- VernacularTitle:人工智能在肾结石成分预测中的研究进展
- Author:
Yongdong PAN
1
;
Yunteng HUANG
;
Guofeng XU
Author Information
1. 上海交通大学医学院附属新华医院小儿泌尿外科，上海 200092
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Machine learning;
Kidney calculi;
Composition analysis
- From:
Chinese Journal of Urology
2025;46(2):157-160
- CountryChina
- Language:Chinese
-
Abstract:
Kidney stone disease (KSD) is a common urinary system disorder characterized by a complex composition of stones. Identifying the composition of kidney stones is of great significance for their treatment and prevention. With the application of artificial intelligence techniques such as machine learning and deep learning, efficient and accurate identification of the composition of kidney stones has been achieved, greatly improving the efficiency, accuracy, and preeminence of stone identification. It also provides new ideas and methods for the treatment of kidney stones. This article reviewed the literatures from three aspects: the clinical data of stone patients, medical images of stones, and direct visual images, combined with artificial intelligence. It has been found that most studies have achieved accurate predictions of the main components of stones, such as calcium stones or uric acid stones. However, due to limitations in the dataset and the complexity of stone compositions, there is still great potential in the identification and prediction of the composition of kidney stones using artificial intelligence.
