文献类型：期刊文献

中文题名：人工智能在骨质疏松症中应用的研究进展

英文题名：Research progress in the application of artificial intelligence in osteoporosis

作者：史凡凡[1];赵继荣[1];马同[2];赵宁[2];陈文[2];朱宝[2];薛旭[2];雒永生[2]

第一作者：史凡凡

机构：[1]甘肃中医药大学,甘肃兰州700030;[2]甘肃省中医院,甘肃兰州730050

第一机构：甘肃中医药大学

年份：2023

卷号：29

期号：7

起止页码：1047

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2020】；CSCD:【CSCD2023_2024】；

基金：甘肃省科技计划重大项目(21ZD4FA009);甘肃省中医药项目(GZKZ-2020-3);甘肃省自然科学基金(21JR1RA050)。

语种：中文

中文关键词：骨质疏松症;骨质疏松性骨折;人工智能;机器学习;深度学习

外文关键词：osteoporosis;osteoporotic fracture;artificial intelligence;machine learning;deep learning

摘要：骨质疏松症(osteoporosis, OP)是一种与增龄相关的骨骼疾患,其起病隐匿,呈渐进性发展,患者初期无明显的临床表现,但随着病情的进展,骨量不断流失及骨组织微结构破坏,进而出现骨痛、脊柱变形,甚至出现骨质疏松性骨折(osteoporotic fracture, OPF)等严重并发症。双能X线吸收法是目前临床诊断OP的金标准,但由于其诊断的准确度受到体重、腰椎退行性改变及主动脉壁钙化等因素的影响,存在假阴性诊断的可能。近年来,人工智能(artificial intelligence, AI)在医学领域快速发展,目前AI已广泛应用于OP的研究中,其在OP筛查、诊断及预测领域的研究已成为一个新的热点。该文从AI应用于OP的早期筛查、医学影像学表现、临床诊疗资料及OPF风险预测等4个方面,阐述AI在OP诊疗过程中的应用现状及优势,为OP的精准诊疗提供新方向。
Osteoporosis(OP)is a kind of bone disease related to aging.Its onset is obscure and development is gradual.Patients are without obvious clinical manifestations in the early stage.However,with the progress of the disease,bone loss and bone tissue microstructure destruction occur,and bone pain,spine deformation,and even osteoporotic fracture(OPF)and other serious complications may develop.Dual-energy X-ray absorption method is the current gold standard for clinical diagnosis of OP.Because its diagnostic accuracy is affected by the factors of body weight,lumbar degenerative changes,and aortic wall calcification,a false negative diagnosis is possible.In recent years,artificial intelligence(AI)has developed rapidly in the medical field.At present,AI has been widely used in the research of OP.Its research in the field of OP screening,diagnosis,and prediction has become a new hot spot.This paper expounds the application status and advantages of AI in OP diagnosis and treatment from four aspects,i.e.,OP early screening,medical imaging performance,clinical diagnosis and treatment data,and OPF risk prediction,so as to provide a new direction for the accurate diagnosis and treatment of OP.