机器学习在骨质疏松症影像学应用的研究

Progress in the application of machine learning in osteoporosis imaging

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.