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 quan...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∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy 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,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In 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 P>0.05).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 P<0.001).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.展开更多
目的:探讨MSCT和MR肺动脉成像用于急性肺动脉栓塞诊断临床价值差异。方法:回顾性选取我院2015年7月—2019年6月收治急性肺动脉栓塞患者共50例临床资料,均行M S C T和M R肺动脉成像,比较两种肺动脉成像方法栓塞部位检出情况及间接征象阳...目的:探讨MSCT和MR肺动脉成像用于急性肺动脉栓塞诊断临床价值差异。方法:回顾性选取我院2015年7月—2019年6月收治急性肺动脉栓塞患者共50例临床资料,均行M S C T和M R肺动脉成像,比较两种肺动脉成像方法栓塞部位检出情况及间接征象阳性率。结果:M S C T肺动脉成像检出患者单发栓塞、双侧多发栓塞、左侧多发栓塞及右侧多发栓塞比例分别为20.00%,56.00%,14.00%,10.00%;M S C T肺动脉成像检出患者单发栓塞、双侧多发栓塞、左侧多发栓塞及右侧多发栓塞比例分别为24.00%,34.00%,10.00%,12.00%;两种肺动脉成像方法栓塞部位检出情况比较差异无显著性(P>0.05);M S C T肺动脉成像检出患者马赛克、积液、肺动脉高压、肺段梗死及肺纹理稀疏比例分别为28.00%,24.00%,12.00%,26.00%,12.00%;MSCT肺动脉成像检出患者马赛克、积液、肺动脉高压、肺段梗死及肺纹理稀疏比例分别为26.00%,20.00%,16.00%,24.00%,10.00%;两种肺动脉成像方法间接征象阳性率比较差异无显著性(P>0.05)。结论:M R肺动脉成像用于急性肺动脉栓塞诊断价值与M S C T肺动脉成像接近;M S C T肺动脉成像操作简单,可清晰显示病变直接征象和间接征象;而M R肺动脉成像则能够清晰显示肺叶动脉以上栓子,但对于肺段动脉栓子需结合三维增强MR动脉成像。展开更多
文摘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∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy 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,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In 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 P>0.05).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 P<0.001).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.
文摘目的:探讨MSCT和MR肺动脉成像用于急性肺动脉栓塞诊断临床价值差异。方法:回顾性选取我院2015年7月—2019年6月收治急性肺动脉栓塞患者共50例临床资料,均行M S C T和M R肺动脉成像,比较两种肺动脉成像方法栓塞部位检出情况及间接征象阳性率。结果:M S C T肺动脉成像检出患者单发栓塞、双侧多发栓塞、左侧多发栓塞及右侧多发栓塞比例分别为20.00%,56.00%,14.00%,10.00%;M S C T肺动脉成像检出患者单发栓塞、双侧多发栓塞、左侧多发栓塞及右侧多发栓塞比例分别为24.00%,34.00%,10.00%,12.00%;两种肺动脉成像方法栓塞部位检出情况比较差异无显著性(P>0.05);M S C T肺动脉成像检出患者马赛克、积液、肺动脉高压、肺段梗死及肺纹理稀疏比例分别为28.00%,24.00%,12.00%,26.00%,12.00%;MSCT肺动脉成像检出患者马赛克、积液、肺动脉高压、肺段梗死及肺纹理稀疏比例分别为26.00%,20.00%,16.00%,24.00%,10.00%;两种肺动脉成像方法间接征象阳性率比较差异无显著性(P>0.05)。结论:M R肺动脉成像用于急性肺动脉栓塞诊断价值与M S C T肺动脉成像接近;M S C T肺动脉成像操作简单,可清晰显示病变直接征象和间接征象;而M R肺动脉成像则能够清晰显示肺叶动脉以上栓子,但对于肺段动脉栓子需结合三维增强MR动脉成像。
