基于CT机器学习在尿石症中的研究进展  

Research Progress of CT-Based Machine Learning in Urolithiasis

作　　者：周聪 王亚洲[1] 朱永月 王默涵 廖雯欣 王道清[1] ZHOU Cong;WANG Yazhou;ZHU Yongyue;WANG Mohan;LIAO Wenxin;WANG Daoqing(Department of Radiology,The First Affiliated Hospital of Henan University of Chinese Medicine;First School of Clinical Medicine,Henan University of Chinese Medicine;Thrid School of Clinical Medicine,Henan University of Chinese Medicine)

机构地区：[1]河南中医药大学第一附属医院放射科 [2]河南中医药大学第一临床医学院 [3]河南中医药大学第三临床医学院

出　　处：《中国医学计算机成像杂志》2024年第5期641-645,共5页Chinese Computed Medical Imaging

摘　　要：尿石症是泌尿系统最常见的疾病之一,CT是其术前诊断的首选检查方案。目前,机器学习(ML)被广泛应用于医学影像领域当中,基于ML的CT图像分析技术已经在尿石的自动分割、鉴别诊断、成分分析及术后疗效预测等方面表现出较好的效能。本文阐述CT图像的ML方法在尿石症中的研究进展,旨在指导和优化临床决策过程,以实现精准医疗。Urolithiasis is one of the most common diseases of the urinary system,and CT is the first choice for its preoperative diagnosis.At present,machine learning(ML)is widely used in the field of medical imaging,and ML-based CT image analysis technology has shown good efficacy in automatic segmentation,differential diagnosis,component analysis and postoperative curative effect prediction of urolithiths.This paper describes the research progress of ML methods of CT images in urolithiasis,aiming to guide and optimize the clinical decision-making process to achieve precision medicine.

关 键 词：机器学习 尿石症 泌尿系结石 计算机体层成像 影像组学 

分 类 号：R691.4[医药卫生—泌尿科学]