Objective To investigate the value of polar residual network(PResNet)model for assisting evaluation on rat myocardial infarction(MI)segment in myocardial contrast echocardiography(MCE).Methods Twenty-five male SD rats...Objective To investigate the value of polar residual network(PResNet)model for assisting evaluation on rat myocardial infarction(MI)segment in myocardial contrast echocardiography(MCE).Methods Twenty-five male SD rats were randomly divided into MI group(n=15)and sham operation group(n=10).MI models were established in MI group through ligation of the left anterior descending coronary artery using atraumatic suture,while no intervention was given to those in sham operation group after thoracotomy.MCE images of both basal and papillary muscle levels on the short axis section of left ventricles were acquired after 1 week,which were assessed independently by 2 junior and 2 senior ultrasound physicians.The evaluating efficacy of MI segment,the mean interpretation time and the consistency were compared whether under the assistance of PResNet model or not.Results No significant difference of efficacy of evaluation on MI segment was found for senior physicians with or without assistance of PResNet model(both P>0.05).Under the assistance of PResNet model,the efficacy of junior physicians for diagnosing MI segment was significantly improved compared with that without the assistance of PResNet model(both P<0.01),and was comparable to that of senior physicians.Under the assistance of PResNet model,the mean interpretation time of each physician was significantly shorter than that without assistance(all P<0.001),and the consistency between junior physicians and among junior and senior physicians were both moderate(Kappa=0.692,0.542),which became better under the assistance(Kappa=0.763,0.749).Conclusion PResNet could improve the efficacy of junior physicians for evaluation on rat MI segment in MCE images,shorten interpretation time with different aptitudes,also improve the consistency to some extent.展开更多
Objective:Cardiovascular disease(CVD)remains a significant public health challenge in China.Accurateperception of individual CVD risk is crucial for timely intervention and preventive strategies.This studyaimed to det...Objective:Cardiovascular disease(CVD)remains a significant public health challenge in China.Accurateperception of individual CVD risk is crucial for timely intervention and preventive strategies.This studyaimed to determine the alignment between CVD risk perception levels and objectively calculated CVDrisk levels,then investigate the disparity in physical activity and healthy diet habits among distinct CVDrisk perception categories.Methods:From March to August 2022,a cross-sectional survey was conducted in Zhejiang Province usingconvenience sampling.Participants aged between 20 and 80 years,without prior diagnosis of CVD wereincluded.CVD risk perception was evaluated with the Chinese version of the Attitude and Beliefs aboutCardiovascular Disease Risk Perception Questionnaire,while objective CVD risk was assessed through thePrediction for Atherosclerotic Cardiovascular Disease Risk(China-PAR)model.Participants’demographicinformation,self-reported physical activity,and healthy diet score were also collected.Results:A total of 739 participants were included in the final analysis.Less than a third of participants(29.2%)accurately perceived their CVD risk,while 64.5%over-perceived it and 6.2%under-perceived it.Notably,half of the individuals(50.0%)with high CVD risk under-perceived their actual risk.Compared tothe accurate perception group,individuals aged 60e80 years old(OR=6.569),currently drinking(OR=3.059),and with hypertension(OR=2.352)were more likely to under-perceive their CVD risk.Meanwhile,participants aged 40-<60 years old(OR=2.462)and employed(OR=2.352)were morelikely to over-perceive their risk.The proportion of individuals engaging in physical activity was lowest inthe under-perception group,although the difference among the three groups was not statistically significant(X^(2)=2.556,P=0.278).In addition,the proportion of individuals practicing healthy diet habitswas also lowest in the under-perception group,and a significant statistical difference was observedamong the three groups(X^(2)=10.310,P=0.006).Conclusion:Only a small proportion of participants accurately perceived their CVD risk,especially amongthose with high actual CVD risk.Individuals in the under-perceived CVD risk group exhibited the lowestrates of physical activity engagement and healthy diet adherence.Healthcare professionals should prioritize implementing personalized CVD risk communication strategies tailored to specific subgroups toenhance the accuracy of risk perception.展开更多
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
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
文摘Objective To investigate the value of polar residual network(PResNet)model for assisting evaluation on rat myocardial infarction(MI)segment in myocardial contrast echocardiography(MCE).Methods Twenty-five male SD rats were randomly divided into MI group(n=15)and sham operation group(n=10).MI models were established in MI group through ligation of the left anterior descending coronary artery using atraumatic suture,while no intervention was given to those in sham operation group after thoracotomy.MCE images of both basal and papillary muscle levels on the short axis section of left ventricles were acquired after 1 week,which were assessed independently by 2 junior and 2 senior ultrasound physicians.The evaluating efficacy of MI segment,the mean interpretation time and the consistency were compared whether under the assistance of PResNet model or not.Results No significant difference of efficacy of evaluation on MI segment was found for senior physicians with or without assistance of PResNet model(both P>0.05).Under the assistance of PResNet model,the efficacy of junior physicians for diagnosing MI segment was significantly improved compared with that without the assistance of PResNet model(both P<0.01),and was comparable to that of senior physicians.Under the assistance of PResNet model,the mean interpretation time of each physician was significantly shorter than that without assistance(all P<0.001),and the consistency between junior physicians and among junior and senior physicians were both moderate(Kappa=0.692,0.542),which became better under the assistance(Kappa=0.763,0.749).Conclusion PResNet could improve the efficacy of junior physicians for evaluation on rat MI segment in MCE images,shorten interpretation time with different aptitudes,also improve the consistency to some extent.
基金received funding from Science and Technology Department of Zhejiang Province(Grant No.LGF21H170001)the Health Commission of Zhejiang Province(Grant No.2023KY759)Hospital Management soft science research project of Kangenbei in Zhejiang province(2023ZHA-KEB104).
文摘Objective:Cardiovascular disease(CVD)remains a significant public health challenge in China.Accurateperception of individual CVD risk is crucial for timely intervention and preventive strategies.This studyaimed to determine the alignment between CVD risk perception levels and objectively calculated CVDrisk levels,then investigate the disparity in physical activity and healthy diet habits among distinct CVDrisk perception categories.Methods:From March to August 2022,a cross-sectional survey was conducted in Zhejiang Province usingconvenience sampling.Participants aged between 20 and 80 years,without prior diagnosis of CVD wereincluded.CVD risk perception was evaluated with the Chinese version of the Attitude and Beliefs aboutCardiovascular Disease Risk Perception Questionnaire,while objective CVD risk was assessed through thePrediction for Atherosclerotic Cardiovascular Disease Risk(China-PAR)model.Participants’demographicinformation,self-reported physical activity,and healthy diet score were also collected.Results:A total of 739 participants were included in the final analysis.Less than a third of participants(29.2%)accurately perceived their CVD risk,while 64.5%over-perceived it and 6.2%under-perceived it.Notably,half of the individuals(50.0%)with high CVD risk under-perceived their actual risk.Compared tothe accurate perception group,individuals aged 60e80 years old(OR=6.569),currently drinking(OR=3.059),and with hypertension(OR=2.352)were more likely to under-perceive their CVD risk.Meanwhile,participants aged 40-<60 years old(OR=2.462)and employed(OR=2.352)were morelikely to over-perceive their risk.The proportion of individuals engaging in physical activity was lowest inthe under-perception group,although the difference among the three groups was not statistically significant(X^(2)=2.556,P=0.278).In addition,the proportion of individuals practicing healthy diet habitswas also lowest in the under-perception group,and a significant statistical difference was observedamong the three groups(X^(2)=10.310,P=0.006).Conclusion:Only a small proportion of participants accurately perceived their CVD risk,especially amongthose with high actual CVD risk.Individuals in the under-perceived CVD risk group exhibited the lowestrates of physical activity engagement and healthy diet adherence.Healthcare professionals should prioritize implementing personalized CVD risk communication strategies tailored to specific subgroups toenhance the accuracy of risk perception.
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.
