文献类型：期刊文献

中文题名：机器学习在骨质疏松症影像学应用的研究

英文题名：Progress in the application of machine learning in osteoporosis imaging

作者：马同[1];赵继荣[2];薛旭[1];史凡凡[2];赵宁[1];杨涛[3];叶丙霖[4]

第一作者：马同

机构：[1]甘肃省中医院,甘肃兰州730050;[2]甘肃中医药大学,甘肃兰州700030;[3]甘肃中医药大学附属医院,甘肃兰州730030;[4]甘肃省中医药研究院,甘肃兰州730050

第一机构：甘肃省中医院,甘肃兰州730050

年份：2024

卷号：30

期号：3

起止页码：401

中文期刊名：中国骨质疏松杂志

外文期刊名：Chinese Journal of Osteoporosis

收录：CSTPCD;;北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；

基金：甘肃省科技计划重大项目(21ZD4FA009);甘肃省联合基金重大项目(23JRRA1519);赵继荣甘肃省名中医传承工作室建设项目;甘肃省临床医学研究中心建设项目(2020?0411?SFC?0053);甘肃省自然科学基金(21JR1RA050、23JRRA1248、23JRRA1245);甘肃省中医药项目(GZKZ?2020?3);兰州市科技计划项目(2019?ZD?116)。

语种：中文

中文关键词：机器学习;深度学习;骨质疏松症;骨质疏松性骨折;综述

外文关键词：machine learning;deep learning;osteoporosis;osteoportic fracture;review

摘要：骨质疏松症(osteoporosis,OP)是影响中老年人健康的主要问题之一,其最严重的并发症骨质疏松性骨折(osteoporotic fracture,OPF)亦存在较高的致残率及致死率。OP的精准筛查与诊断主要依靠影像学分析,而且诊断结果依赖医师的主观判断,因此传统的影像学手段对OP的精准筛查、诊断与鉴别存在一定的挑战性。机器学习(machine learning,ML)与影像学图像的交叉融合,有效提高了临床医生的诊断效率,同时实现了对未知疾患(如OPF)的预测。近年来,基于ML模型利用影像学数据在OP的筛查与诊断及OPF的诊断与预测方面取得了显著的进展。本文将对近年基于影像学资料的ML模型辅助筛查与诊断OP的相关研究进行综述,为临床精准诊疗OP提供新思路。
Osteoporosis(OP)is one of the main problems affecting the health of middle?aged and elderly people,and its most serious complication is osteoporotic fracture(OPF),which also has a high disability and mortality rate.The accurate screening and diagnosis of OP mainly rely on imaging analysis,and the diagnosis result rely on the subjective judgment of doctors.Therefore,the traditional imaging method of OP have certain challenges to the accurate screening,diagnosis and differentiation of OP.The cross?fusion of machine learning(ML)and imaging images effectively improves the diagnostic efficiency of clinicians,while realizing the prediction of unknown diseases(such as OPF).Recently,significant progress has been made in the screening and diagnosis of OP and in the diagnosis and prediction of OPF.This paper will review the recent related studies on ML model?assisted screening and diagnosis of OP based on imaging data,so as to provide new ideas for accurate clinical diagnosis and treatment of OP.