Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided i...Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.展开更多
目的:研究血清癌胚抗原(CEA)、细胞角蛋白19片段21-1(CYFRA21-1)、神经元特异性烯醇化酶(NSE)在肺癌临床诊断中的应用价值。方法:本研究共入选332例研究对象,分为肺癌组(n=112)、肺部良性疾病组(n=132)和健康组(n=88),采用电化学发光免...目的:研究血清癌胚抗原(CEA)、细胞角蛋白19片段21-1(CYFRA21-1)、神经元特异性烯醇化酶(NSE)在肺癌临床诊断中的应用价值。方法:本研究共入选332例研究对象,分为肺癌组(n=112)、肺部良性疾病组(n=132)和健康组(n=88),采用电化学发光免疫分析技术分别检测上述各组患者的血清CEA、CYFRA21-1、NSE水平。结果:肺癌组的血清CEA(ng/ml)、NSE(ng/ml)和CYFRA21-1(ng/ml)水平均较肺部良性疾病组和健康组明显升高,且差异(41.26±22.84 VS 5.89±2.48 VS 2.16±1.2630.12±17.39 VS 15.37±9.48 VS 11.39±6.2121.27±10.36 VS 2.97±1.33 VS 2.02±1.23,P<0.05)。肺癌组血清肿瘤标志物检测阳性率均较肺部良性疾病组明显高(P<0.05)肺腺癌组的CEA检测阳性率(74.55%)较其他组明显高(P<0.05),肺小细胞癌组的NSE的阳性检测率(67.74%)也较其他组明显高(P<0.05)。CEA、CYFRA21-1和NSE三者联合的检测敏感性(96.43%)最高,且与其他肿瘤标志物组合比较具有统计学差异(P<0.05);三者联合检测特异性(80.91%)与其他肿瘤标志物组合相比较没有统计学差异(P>0.05)。肺癌转移组与未转移组血清CEA水平具有统计学差异(31.15±12.48 VS3.12±2.12,P<0.05)。结论:联合血清CEA、CYFRA21-1、NSE具有更高的肺癌诊断价值,血清CEA水平对肺癌转移具有一定的辅助检测价值。展开更多
文摘Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.
文摘目的:研究血清癌胚抗原(CEA)、细胞角蛋白19片段21-1(CYFRA21-1)、神经元特异性烯醇化酶(NSE)在肺癌临床诊断中的应用价值。方法:本研究共入选332例研究对象,分为肺癌组(n=112)、肺部良性疾病组(n=132)和健康组(n=88),采用电化学发光免疫分析技术分别检测上述各组患者的血清CEA、CYFRA21-1、NSE水平。结果:肺癌组的血清CEA(ng/ml)、NSE(ng/ml)和CYFRA21-1(ng/ml)水平均较肺部良性疾病组和健康组明显升高,且差异(41.26±22.84 VS 5.89±2.48 VS 2.16±1.2630.12±17.39 VS 15.37±9.48 VS 11.39±6.2121.27±10.36 VS 2.97±1.33 VS 2.02±1.23,P<0.05)。肺癌组血清肿瘤标志物检测阳性率均较肺部良性疾病组明显高(P<0.05)肺腺癌组的CEA检测阳性率(74.55%)较其他组明显高(P<0.05),肺小细胞癌组的NSE的阳性检测率(67.74%)也较其他组明显高(P<0.05)。CEA、CYFRA21-1和NSE三者联合的检测敏感性(96.43%)最高,且与其他肿瘤标志物组合比较具有统计学差异(P<0.05);三者联合检测特异性(80.91%)与其他肿瘤标志物组合相比较没有统计学差异(P>0.05)。肺癌转移组与未转移组血清CEA水平具有统计学差异(31.15±12.48 VS3.12±2.12,P<0.05)。结论:联合血清CEA、CYFRA21-1、NSE具有更高的肺癌诊断价值,血清CEA水平对肺癌转移具有一定的辅助检测价值。
