Heart failure has gained increasing notice due to its high prevalence and mortality rate. The management for heart failure has been emphasized on the role of device therapy. Implantable cardioverter defibrillator(ICD)...Heart failure has gained increasing notice due to its high prevalence and mortality rate. The management for heart failure has been emphasized on the role of device therapy. Implantable cardioverter defibrillator(ICD) and cardiac resynchronization therapy(CRT) were given strong recommendation for heart failure with reduced ejection fraction(HFrEF), considering their effectiveness on preventing sudden cardiac death(SCD), improving cardiac function and benefiting survival. In this review, we explained the underlying mechanisms of disease initiation and progression in HFrEF, in order to build the connection between the pathological basis of HFrEF and the rationality of ICD and CRT on terminating ventricular arrhythmia, improving cardiac function, decreasing the rate of adverse clinical outcomes and benefiting survival. In addition, we discussed the high-quality researches with significant values on the discovery of device therapy clinical benefits, and compared the class I recommendations for device therapy in HFr EF, suggested by American Heart Association and European Society of Cardiology.展开更多
Anew method in system design of ejecting devices of missiles is first presented.Some important points are dis-cussed,which guid the research and development of new ejecting devices of missileg,amd provid the foundatio...Anew method in system design of ejecting devices of missiles is first presented.Some important points are dis-cussed,which guid the research and development of new ejecting devices of missileg,amd provid the foundation flr thw design of mew ejecting device is provided.The system design includes the distribution of techmology specifica-tion,3-D solid modeling of ejecting devices of missiles im-ported from abroad,the design of pmeumatic device sys-tem,the design of ejecting mechanism system,the predic-tion of reliability and the experimental analysis,etc.展开更多
Background Accurate measurement of left ventricular ejection fraction(LVEF)is crucial in diagnosing and managing cardiac conditions.Deep learning(DL)models offer potential to improve the consistency and efficiency of ...Background Accurate measurement of left ventricular ejection fraction(LVEF)is crucial in diagnosing and managing cardiac conditions.Deep learning(DL)models offer potential to improve the consistency and efficiency of these measurements,reducing reliance on operator expertise.Objective The aim of this study was to develop an innovative software-hardware combined device,featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images.Methods A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups.Another 500 patients from another campus were prospectively collected as external validation group.The age,sex,reason for echocardiography and the type of patients were collected.Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment,apical four-chamber view images were labeled manually and utilized for training our deep learning framework.This system combined convolutional neural networks(CNN)with transformers for enhanced image recognition and analysis.Combined with the model that was named QHAutoEF,a‘one-touch’software module was developed and integrated into the echocardiography hardware,providing intuitive,realtime visualization of LVEF measurements.The device’s performance was evaluated with metrics such as the Dice coefficient and Jaccard index,along with computational efficiency indicators.The dice index,intersection over union,size,floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures.Bland-Altman analysis and the receiver operating characteristic(ROC)curve were used for validation of the accuracy of the model.The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups.Results Patients from external validation group were older than those from training group((60±14)years vs.(55±16)years,respectively,P<0.001).The gender distribution among three groups were showed no statistical difference(43%vs.42%vs.50%,respectively,P=0.095).Significant differences were showed among patients with different type(all P<0.001)and reason for echocardiography(all P<0.001 except for other reasons).QHAutoEF achieved a high Dice index(0.942 at end-diastole,0.917 at end-systole)with a notably compact model size(10.2 MB)and low computational cost(93.86 G floating point operations(FLOPs)).It exhibited high consistency with expert manual measurements(intraclass correlation coefficient(ICC)=0.90(0.89,0.92),P<0.001)and excellent capability to differentiate patients with LVEF≥60%from those with reduced function,yielding an area under the operation curve(AUC)of 0.92(0.90–0.95).Subgroup analysis showed a good correlation between QHAutoEF results and manual results from experienced experts among patients of different types(R=0.93,0.73,0.92,respectively,P<0.001)and ages(R=0.92,0.94,0.89,0.91,0.81,respectively,P<0.001).Conclusions Our software-hardware device offers an improved solution for the automated measurement of LVEF,demonstrating not only high accuracy and consistency with manual expert measurements but also practical adaptability for clinical settings.This device might potentially support clinicians and augment clinical decision.展开更多
基金supported by grants from Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention (No. Y0120220151)Science and Technology Projects of Guangzhou (No. 201903010097)+1 种基金National Natural Science Foundation of China (No. 82002014)Natural Science Foundation of Guangdong Province (No. 2021A1515010107)。
文摘Heart failure has gained increasing notice due to its high prevalence and mortality rate. The management for heart failure has been emphasized on the role of device therapy. Implantable cardioverter defibrillator(ICD) and cardiac resynchronization therapy(CRT) were given strong recommendation for heart failure with reduced ejection fraction(HFrEF), considering their effectiveness on preventing sudden cardiac death(SCD), improving cardiac function and benefiting survival. In this review, we explained the underlying mechanisms of disease initiation and progression in HFrEF, in order to build the connection between the pathological basis of HFrEF and the rationality of ICD and CRT on terminating ventricular arrhythmia, improving cardiac function, decreasing the rate of adverse clinical outcomes and benefiting survival. In addition, we discussed the high-quality researches with significant values on the discovery of device therapy clinical benefits, and compared the class I recommendations for device therapy in HFr EF, suggested by American Heart Association and European Society of Cardiology.
文摘Anew method in system design of ejecting devices of missiles is first presented.Some important points are dis-cussed,which guid the research and development of new ejecting devices of missileg,amd provid the foundation flr thw design of mew ejecting device is provided.The system design includes the distribution of techmology specifica-tion,3-D solid modeling of ejecting devices of missiles im-ported from abroad,the design of pmeumatic device sys-tem,the design of ejecting mechanism system,the predic-tion of reliability and the experimental analysis,etc.
文摘Background Accurate measurement of left ventricular ejection fraction(LVEF)is crucial in diagnosing and managing cardiac conditions.Deep learning(DL)models offer potential to improve the consistency and efficiency of these measurements,reducing reliance on operator expertise.Objective The aim of this study was to develop an innovative software-hardware combined device,featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images.Methods A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups.Another 500 patients from another campus were prospectively collected as external validation group.The age,sex,reason for echocardiography and the type of patients were collected.Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment,apical four-chamber view images were labeled manually and utilized for training our deep learning framework.This system combined convolutional neural networks(CNN)with transformers for enhanced image recognition and analysis.Combined with the model that was named QHAutoEF,a‘one-touch’software module was developed and integrated into the echocardiography hardware,providing intuitive,realtime visualization of LVEF measurements.The device’s performance was evaluated with metrics such as the Dice coefficient and Jaccard index,along with computational efficiency indicators.The dice index,intersection over union,size,floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures.Bland-Altman analysis and the receiver operating characteristic(ROC)curve were used for validation of the accuracy of the model.The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups.Results Patients from external validation group were older than those from training group((60±14)years vs.(55±16)years,respectively,P<0.001).The gender distribution among three groups were showed no statistical difference(43%vs.42%vs.50%,respectively,P=0.095).Significant differences were showed among patients with different type(all P<0.001)and reason for echocardiography(all P<0.001 except for other reasons).QHAutoEF achieved a high Dice index(0.942 at end-diastole,0.917 at end-systole)with a notably compact model size(10.2 MB)and low computational cost(93.86 G floating point operations(FLOPs)).It exhibited high consistency with expert manual measurements(intraclass correlation coefficient(ICC)=0.90(0.89,0.92),P<0.001)and excellent capability to differentiate patients with LVEF≥60%from those with reduced function,yielding an area under the operation curve(AUC)of 0.92(0.90–0.95).Subgroup analysis showed a good correlation between QHAutoEF results and manual results from experienced experts among patients of different types(R=0.93,0.73,0.92,respectively,P<0.001)and ages(R=0.92,0.94,0.89,0.91,0.81,respectively,P<0.001).Conclusions Our software-hardware device offers an improved solution for the automated measurement of LVEF,demonstrating not only high accuracy and consistency with manual expert measurements but also practical adaptability for clinical settings.This device might potentially support clinicians and augment clinical decision.
