Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE vid...Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAX GV).The videos were divided into development set(245 A4C,155 PLAX,225 PSAX GV),semi-automated training set(98 A4C,62 PLAX,90 PSAX GV)and test set(147 A4C,93 PLAX,135 PSAX GV)at the ratio of 5∶2∶3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33%,97.26%,97.26%and 97.26%to 99.73%,99.65%,99.77%and 99.71%,respectively,while the overall accuracy for assessing A4C,PLAX and PSAX GV TTE as standard views in test set improved from 89.12%,83.87%and 90.37%to 93.20%,90.32%and 93.33%,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.展开更多
目的分析不同体重指数(body mass index,BMI)大学生的舌象参数特征,以期为消瘦、超重肥胖人群的临床辨识和诊疗提供一定的舌象客观化依据。方法使用TFDA-1型舌诊仪采集386例体检大学生的舌象图片,根据BMI分为消瘦组、超重肥胖组和体重...目的分析不同体重指数(body mass index,BMI)大学生的舌象参数特征,以期为消瘦、超重肥胖人群的临床辨识和诊疗提供一定的舌象客观化依据。方法使用TFDA-1型舌诊仪采集386例体检大学生的舌象图片,根据BMI分为消瘦组、超重肥胖组和体重正常组,采用舌象数据采集软件提取各组的舌象参数特征,比较各组舌色及舌苔R(红色值)、G(绿色值)、B(蓝色值)、L(明度)、a(绿—红值)、b(蓝—黄值)、H(色调)、S(饱和度)、V(亮度)参数的差异,并运用Spearman检验分析BMI指数与舌象参数的相关性。结果体重正常组舌色红色值、绿—红值、饱和度显著低于消瘦组,显著高于超重肥胖组(P<0.01),舌色亮度值显著大于其余2组(P<0.01);体重正常组苔质明度、绿—红值、亮度值显著高于其余2组(P<0.01),消瘦组舌苔绿—红值、亮度值高于超重肥胖组(P<0.01);3组人群苔色参数无统计学差异(P>0.05)。Spearman相关性分析显示,BMI指数与舌色红色值、舌色绿—红值、舌色饱和度、苔质绿—红值、苔质亮度值呈负相关(P<0.01)。结论消瘦、超重肥胖人群的舌象参数有明显差异,BMI指数与舌象参数具有一定的相关性,舌象客观参数可作为消瘦、超重肥胖人群的辨识标准之一,其中舌色红色值、绿—红值、饱和度,苔质绿—红值、亮度值可作为消瘦、超重肥胖人群与体重正常人群的鉴别点。展开更多
文摘Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAX GV).The videos were divided into development set(245 A4C,155 PLAX,225 PSAX GV),semi-automated training set(98 A4C,62 PLAX,90 PSAX GV)and test set(147 A4C,93 PLAX,135 PSAX GV)at the ratio of 5∶2∶3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33%,97.26%,97.26%and 97.26%to 99.73%,99.65%,99.77%and 99.71%,respectively,while the overall accuracy for assessing A4C,PLAX and PSAX GV TTE as standard views in test set improved from 89.12%,83.87%and 90.37%to 93.20%,90.32%and 93.33%,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.
文摘目的分析不同体重指数(body mass index,BMI)大学生的舌象参数特征,以期为消瘦、超重肥胖人群的临床辨识和诊疗提供一定的舌象客观化依据。方法使用TFDA-1型舌诊仪采集386例体检大学生的舌象图片,根据BMI分为消瘦组、超重肥胖组和体重正常组,采用舌象数据采集软件提取各组的舌象参数特征,比较各组舌色及舌苔R(红色值)、G(绿色值)、B(蓝色值)、L(明度)、a(绿—红值)、b(蓝—黄值)、H(色调)、S(饱和度)、V(亮度)参数的差异,并运用Spearman检验分析BMI指数与舌象参数的相关性。结果体重正常组舌色红色值、绿—红值、饱和度显著低于消瘦组,显著高于超重肥胖组(P<0.01),舌色亮度值显著大于其余2组(P<0.01);体重正常组苔质明度、绿—红值、亮度值显著高于其余2组(P<0.01),消瘦组舌苔绿—红值、亮度值高于超重肥胖组(P<0.01);3组人群苔色参数无统计学差异(P>0.05)。Spearman相关性分析显示,BMI指数与舌色红色值、舌色绿—红值、舌色饱和度、苔质绿—红值、苔质亮度值呈负相关(P<0.01)。结论消瘦、超重肥胖人群的舌象参数有明显差异,BMI指数与舌象参数具有一定的相关性,舌象客观参数可作为消瘦、超重肥胖人群的辨识标准之一,其中舌色红色值、绿—红值、饱和度,苔质绿—红值、亮度值可作为消瘦、超重肥胖人群与体重正常人群的鉴别点。
