Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

作     者：DUAN Wei YANG Guoqing LI Yang SHI Feng YANG Lian XIONG Xin CHEN Bei LI Yong FU Quanshui 段维;杨国庆;李洋;石峰;杨连;熊鑫;陈蓓;李勇;付泉水

作者机构：Department of Medical ImagingNorth Sichuan Medical CollegeNanchong 637000China Department of RadiologySuining Central HospitalSuining 629000China Department of RadiologySuining Hospital of Traditional Chinese MedicineSuining 629000China Shanghai United Imaging Intelligence Co.Ltd.Shanghai 201807China 

出 版 物：《中国医学影像技术》 (Chinese Journal of Medical Imaging Technology)

年 卷 期：2024年第40卷第8期

页      面：1231-1235页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 100207[医学-影像医学与核医学] 1006[医学-中西医结合] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 100210[医学-外科学(含：普外、骨外、泌尿外、胸心外、神外、整形、烧伤、野战外)] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 100106[医学-放射医学] 100602[医学-中西医结合临床] 10[医学] 

主　　题：osteoporosis bone density tomography,X-ray computed artificial intelligence 

摘      要：Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶*** mean BMD of L1—L3 vertebrae were measured based on *** bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,*** operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of ***-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring *** Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of *** test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.