Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-cham...Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.展开更多
【目的】为了探讨应用4D左心房自动定量分析(4D Auto LAQ)软件测定的左房容积和应变值对鉴别毛细血管前肺动脉高压和毛细血管后肺动脉高压的价值,并与肺-左心房应变偶联指数(ePLAGS)的鉴别能力进行比较。【方法】本研究为回顾性研究,共...【目的】为了探讨应用4D左心房自动定量分析(4D Auto LAQ)软件测定的左房容积和应变值对鉴别毛细血管前肺动脉高压和毛细血管后肺动脉高压的价值,并与肺-左心房应变偶联指数(ePLAGS)的鉴别能力进行比较。【方法】本研究为回顾性研究,共纳入了自2021年7月至2022年4月就诊于中山大学附属第一医院门诊或住院部的98例高概率肺动脉高压(PH)患者,收集所有患者的临床病史及实验室资料。根据肺动脉楔压(PAWP)将所有患者分为2组:毛细血管前PH组(PAWP≤15 mmHg)及毛细血管后PH组(PAWP>15 mmHg)。所纳入的患者均接受了经胸超声心动图检查,并应用4D Auto LAQ软件自动测量左房容积和应变参数。【结果】根据PAWP的数值,入选患者分为两组:毛细血管前PH组[n=39,年龄(53±24)岁]和毛细血管后PH组[n=59,年龄(57±18)岁]。与毛细血管前PH组相比,毛细血管后PH组的最大左心房容量指数(LAVImax)、最小左心房容量指数(LAVImin)和左心房射血前的容量指数(LAVIpreA)显著增加,而左心房储备期纵向应变值(LASr)和左心房导管期纵向应变值(LAScd)明显降低。多元逻辑回归分析显示,LAVImax[OR:1.40;95%CI:(1.052,1.872);P=0.021]和LAScd[OR:1.76;95%CI:(1.183,2.489);P=0.004]是检测毛细血管后PH的强大独立预测因子。ROC曲线分析表明,LAVImax(AUC=0.82,P<0.001)和LAScd(AUC=0.78,P<0.001)在预测毛细血管后PH组方面具有很高的辨别力,其临界值分别为35.69 mL/m^(2)(敏感性86%,特异性74%)和-9%(敏感性80%,特异性70%)。【结论】:应用4D auto LAQ测量的LAVImax和LAScd是区分毛细血管前和毛细血管后PH患者有价值的参数,且其鉴别能力优于肺-左心房应变偶联指数(ePLAGS)。展开更多
文摘Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.
文摘【目的】为了探讨应用4D左心房自动定量分析(4D Auto LAQ)软件测定的左房容积和应变值对鉴别毛细血管前肺动脉高压和毛细血管后肺动脉高压的价值,并与肺-左心房应变偶联指数(ePLAGS)的鉴别能力进行比较。【方法】本研究为回顾性研究,共纳入了自2021年7月至2022年4月就诊于中山大学附属第一医院门诊或住院部的98例高概率肺动脉高压(PH)患者,收集所有患者的临床病史及实验室资料。根据肺动脉楔压(PAWP)将所有患者分为2组:毛细血管前PH组(PAWP≤15 mmHg)及毛细血管后PH组(PAWP>15 mmHg)。所纳入的患者均接受了经胸超声心动图检查,并应用4D Auto LAQ软件自动测量左房容积和应变参数。【结果】根据PAWP的数值,入选患者分为两组:毛细血管前PH组[n=39,年龄(53±24)岁]和毛细血管后PH组[n=59,年龄(57±18)岁]。与毛细血管前PH组相比,毛细血管后PH组的最大左心房容量指数(LAVImax)、最小左心房容量指数(LAVImin)和左心房射血前的容量指数(LAVIpreA)显著增加,而左心房储备期纵向应变值(LASr)和左心房导管期纵向应变值(LAScd)明显降低。多元逻辑回归分析显示,LAVImax[OR:1.40;95%CI:(1.052,1.872);P=0.021]和LAScd[OR:1.76;95%CI:(1.183,2.489);P=0.004]是检测毛细血管后PH的强大独立预测因子。ROC曲线分析表明,LAVImax(AUC=0.82,P<0.001)和LAScd(AUC=0.78,P<0.001)在预测毛细血管后PH组方面具有很高的辨别力,其临界值分别为35.69 mL/m^(2)(敏感性86%,特异性74%)和-9%(敏感性80%,特异性70%)。【结论】:应用4D auto LAQ测量的LAVImax和LAScd是区分毛细血管前和毛细血管后PH患者有价值的参数,且其鉴别能力优于肺-左心房应变偶联指数(ePLAGS)。
