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:应用四维左心室自动定量分析技术(4D Auto LVQ)及无创心肌做功技术监测局部晚期鼻咽癌患者接受诱导化疗后各时间点左心室整体及局部收缩功能的改变,探讨两种技术的应用价值。方法:选取42例鼻咽癌患者为研究对象,分别在化疗前基础状...目的:应用四维左心室自动定量分析技术(4D Auto LVQ)及无创心肌做功技术监测局部晚期鼻咽癌患者接受诱导化疗后各时间点左心室整体及局部收缩功能的改变,探讨两种技术的应用价值。方法:选取42例鼻咽癌患者为研究对象,分别在化疗前基础状态(T0)及化疗第2周期(T2)、第4周期(T4)结束后进行心脏超声检查。常规测量二维超声心动图参数,应用4D Auto LVQ测量四维左心室容积及应变参数,应用无创心肌做功技术获取整体及局部心肌做功指标,比较各参数在化疗不同时间点间的差异并分析其相关性。结果:T4组二维左心室射血分数(2D-LVEF)、二维整体纵向应变(2D-GLS)、四维左心室射血分数(4D-LVEF)、四维整体圆周应变(4D-GCS)出现降低,T2组、T4组的整体做功指数(GWI)、整体有效功(GCW)、整体做功效率(GWE)较T0组出现减低,差异均有统计学意义(P<0.05)。与化疗前相比,化疗后四维整体纵向应变(4D-GLS)、四维整体面积应变(4D-GAS)、GWI呈逐渐减低趋势,差异具有统计学意义(P<0.05)。左心室不同水平局部心肌做功参数中T2组、T4组左心室心肌3个水平的平均局部心肌做功指数(RMWI)及左心室中段平均局部心肌做功效率(RMWE)均较T0组减低,差异有统计学意义(P<0.05),其中以基底段平均RMWI减低为著。相关性分析显示2D-LVEF与4D-LVEF呈强正相关(r=0.71,P<0.01)。四维应变参数与心肌做功参数间存在一定的相关性,但相关性较弱。结论:与常规超声相比,4D Auto LVQ或无创心肌做功技术均能早期发现鼻咽癌患者化疗后的亚临床心功能损伤,值得临床进一步验证。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:应用四维左心室自动定量分析技术(4D Auto LVQ)及无创心肌做功技术监测局部晚期鼻咽癌患者接受诱导化疗后各时间点左心室整体及局部收缩功能的改变,探讨两种技术的应用价值。方法:选取42例鼻咽癌患者为研究对象,分别在化疗前基础状态(T0)及化疗第2周期(T2)、第4周期(T4)结束后进行心脏超声检查。常规测量二维超声心动图参数,应用4D Auto LVQ测量四维左心室容积及应变参数,应用无创心肌做功技术获取整体及局部心肌做功指标,比较各参数在化疗不同时间点间的差异并分析其相关性。结果:T4组二维左心室射血分数(2D-LVEF)、二维整体纵向应变(2D-GLS)、四维左心室射血分数(4D-LVEF)、四维整体圆周应变(4D-GCS)出现降低,T2组、T4组的整体做功指数(GWI)、整体有效功(GCW)、整体做功效率(GWE)较T0组出现减低,差异均有统计学意义(P<0.05)。与化疗前相比,化疗后四维整体纵向应变(4D-GLS)、四维整体面积应变(4D-GAS)、GWI呈逐渐减低趋势,差异具有统计学意义(P<0.05)。左心室不同水平局部心肌做功参数中T2组、T4组左心室心肌3个水平的平均局部心肌做功指数(RMWI)及左心室中段平均局部心肌做功效率(RMWE)均较T0组减低,差异有统计学意义(P<0.05),其中以基底段平均RMWI减低为著。相关性分析显示2D-LVEF与4D-LVEF呈强正相关(r=0.71,P<0.01)。四维应变参数与心肌做功参数间存在一定的相关性,但相关性较弱。结论:与常规超声相比,4D Auto LVQ或无创心肌做功技术均能早期发现鼻咽癌患者化疗后的亚临床心功能损伤,值得临床进一步验证。
