Tesla blood pumps demonstrate a reduced propensity for hemolysis and thrombosis compared with vane blood pumps. Considering the restricted driving force within the secondary flow channel of vane blood pumps, along with the low hydraulic efficiency of conventional Tesla blood pumps and their internal flow characteristics that significantly contribute to hemolysis and thrombosis, this study introduces a set of vanes atop the rotor of the Tesla blood pump. This forms a dual-fluid domain rotor, and an axial dual-outlet volute shell structure is adopted to realize the separation of the fluid domains. Through numerical simulations of the new structure, a comparative analysis was conducted in this study on the internal flow characteristics of double-outlet and single-outlet volute shells, and symmetric and asymmetric cross-sections of the same rotor. The results indicate that the flow field distribution is more uniform under the double-outlet volute shell structure, and overall energy dissipation is decreased. After implementing the double-outlet design, in the asymmetric cross-section, compared with the symmetric cross-section, the fluid velocity gradient and turbulent kinetic energy at the tongue of the septum are reduced, and the fluid velocity gradient at the convergence of the diffuser tube outlets are also decreased. The maximum scalar stress is lower, and the decline in head and efficiency is mitigated. Moreover, compared with the single-outlet volute shell, the hemolysis index in the asymmetric cross-section is reduced. In summary, this paper proposes a novel dual-outlet centrifugal disk blood pumps, which can provide a reference for the structural design and performance optimization of magnetically levitated centrifugal blood pumps.
Heart-Assist Devices ; Humans ; Equipment Design ; Hemolysis ; Computer Simulation

OBJECTIVES: A ventricular assist device (VAD) is an electromechanical device used to assist cardiac blood circulation, which can be employed for the treatment of end-stage heart failure and is most commonly placed in the left ventricle. Despite enhancing perfusion performance, the implantation of left ventricular assist device (LVAD) transforms the local intraventricular flow and thus may increase the risk of thrombogenesis. This study aims to investigate fluid-particle interactions and thromboembolic risk under different LVAD configurations using three-dimensional (3D) reconstruction models, focusing on the effects of outflow tract orientation and blood flow rates. METHODS: A patient-specific end-diastolic 3D reconstruction model was initially constructed in stereo lithography (STL) format using Mimics software based on CT images. Transient numerical simulations were performed to analyze fluid-particle interactions and thromboembolic risks for LVAD with varying outflow tract orientations under 2 flow rates (4 L/min and 5 L/min), using particles of uniform size (2 mm), and a blood flow rate optimization protocol was implemented for this patient. RESULTS: When the LVAD flow rate was 5 L/min, helicity and flow stagnation of the blood flow increased the particle residence time (RT) and the risk of thrombogenesis of the aortic root. The percentage of particles traveling toward the brachiocephalic trunk was up to 20.33%. When the LVAD flow rate was 4 L/min, blood turbulence in the aorta was reduced, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 10.54%. When the LVAD blood flow rate was 5 L/min and the direction of the outflow pipe was optimal, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 11.22%. A 18-month follow-up observation of the patient revealed that the LVAD was in good working order and the patient had no complications related to the implantation of LVAD. CONCLUSIONS Implantation of LVAD results in a higher risk of cerebral infarction; When implanting LVAD with the same outflow tract direction, optimizing flow velocity and outflow tract can reduce the risk of cerebral infarction occurrence.
Heart-Assist Devices/adverse effects* ; Humans ; Heart Failure/physiopathology* ; Blood Flow Velocity ; Thromboembolism/prevention & control* ; Models, Cardiovascular ; Heart Ventricles/physiopathology* ; Imaging, Three-Dimensional

Extracorporeal membrane oxygenation (ECMO) is primarily used in clinical practice to provide continuous extracorporeal respiratory and circulatory support for patients with severe heart and lung failure, thereby sustaining life. It is a key technology for managing severe heart failure and respiratory failure that are difficult to control. With the accumulation of clinical experience in ECMO for circulatory and/or respiratory support, as well as advancements in biomedical engineering technology, more portable and stable ECMO devices have been introduced into clinical use, benefiting an increasing number of critically ill patients. Although ECMO technology has become relatively mature, the timing of ECMO initiation, management of sudden complications, and monitoring and early warning of physiological indicators are critical factors that greatly affect the therapeutic outcomes of ECMO. This article reviews traditional methods and artificial intelligence techniques used in risk assessment related to ECMO, including the latest achievements and research hotspots. Additionally, it discusses future trends in ECMO risk management, focusing on six key areas: multi-center and prospective studies, external validation and standardization of model performance, long-term prognosis considerations, integration of innovative technologies, enhancing model interpretability, and economic cost-effectiveness analysis. This provides a reference for future researchers to build models and explore new research directions.
Extracorporeal Membrane Oxygenation ; Humans ; Artificial Intelligence ; Risk Assessment ; Respiratory Insufficiency/therapy* ; Heart Failure/therapy*

Cardiovascular risk assessment is a basic tenet of the prevention of cardiovascular disease. Conventional risk assessment models require measurements of blood pressure, blood lipids, and other health-related information prior to assessment of risk via regression models. Compared with traditional approaches, fundus photograph-based cardiovascular risk assessment using artificial intelligence (AI) technology is novel, and has the advantages of immediacy, non-invasiveness, easy performance, and low cost. The Health Risk Assessment and Control Committee of the Chinese Preventive Medicine Association, in collaboration with the Chinese Society of Cardiology and the Society of Health Examination, invited multi-disciplinary experts to form a panel to develop the present consensus, which includes relevant theories, progress in research, and requirements for AI model development, as well as applicable scenarios, applicable subjects, assessment processes, and other issues associated with applying AI technology to assess cardiovascular risk based on fundus photographs. A consensus was reached after multiple careful discussions on the relevant research, and the needs of the health management industry in China and abroad, in order to guide the development and promotion of this new technology.
Humans ; Cardiovascular Diseases/prevention & control* ; Artificial Intelligence ; Consensus ; Risk Factors ; Heart Disease Risk Factors

The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of blade parameters, an optimization design for existing pump blades is conducted. The number of blades, outlet angle, and blade thickness were selected as design variables, with the maximum shear stress within the pump serving as the optimization objective. A back propagation (BP) neural network prediction model was established using existing simulation data, and a grey wolf optimization algorithm was employed to optimize the blade parameters. The results indicated that the optimized blade parameters consisted of 7 impeller blades, an outlet angle of 25 °, and a blade thickness of 1.2 mm; this configuration achieved a maximum shear stress value of 377 Pa-representing a reduction of 16% compared to the original model. Simulation analysis revealed that in comparison to the original model, regions with high shear stress at locations such as the outer edge, root, and base significantly decreased following optimization efforts, thus leading to marked improvements in hemolytic performance. The coupling algorithm employed in this study has significantly reduced the workload associated with modeling and simulation, while also enhancing the performance of optimization objectives. Compared to traditional optimization algorithms, it demonstrates distinct advantages, thereby providing a novel approach for investigating parameter optimization issues related to centrifugal artificial heart pumps.
Neural Networks, Computer ; Algorithms ; Heart-Assist Devices ; Hemolysis ; Humans ; Equipment Design ; Stress, Mechanical ; Computer Simulation

Heart failure (HF),a chronic progressive disease,is a global health problem and the leading cause of deaths in the global population.The pathophysiological abnormalities of HF mainly include abnormal cardiac structure (myocardium and valves),disturbance of electrophysiological activities,and weakened myocardial contractility.In addition to drug therapy and heart transplantation,interventional therapies can be employed for advanced-stage HF,including transcatheter interventions and mechanical circulatory assist devices.This article introduces the devices used for advanced HF that have been marketed or certified as innovative or breakthrough devices around the world and summarizes the research status and prospects the trend in this field.As diversified combinations of HF devices are used for the treatment of advanced HF,considerations regarding individualized HF therapy,risk-benefit evaluation on device design,medical insurance payment,post-market supervision system,and protection of intellectual property rights of high-end technology are needed,which will boost the development of the technology and industry and benefit the patients.
Humans ; Heart-Assist Devices ; Heart Failure/therapy* ; Heart Transplantation ; Myocardium ; Chronic Disease

Objective: To evaluate the long-term efficacy and safety of the implantable ventricular assist system EVAHEART I in clinical use. Methods: Fifteen consecutive patients with end-stage heart failure who received left ventricular assist device therapy in Fuwai Hospital from January 2018 to December 2021 were enrolled in this study, their clinical data were retrospectively analyzed. Cardiac function, liver and kidney function, New York Heart Association (NYHA) classification, 6-minute walk distance and quality of life were evaluated before implantation and at 1, 6, 12, 24 and 36 months after device implantation. Drive cable infection, hemolysis, cerebrovascular events, mechanical failure, abnormally high-power consumption and abnormal pump flow were recorded during follow up. Results: All 15 patients were male, mean average age was (43.0±7.5) years, including 11 cases of dilated cardiomyopathy, 2 cases of ischemic cardiomyopathy, and 2 cases of valvular heart disease. All patients were hemodynamically stable on more than one intravenous vasoactive drugs, and 3 patients were supported by preoperative intra aortic balloon pump (IABP). Compared with before device implantation, left ventricular end-diastolic dimension (LVEDD) was significantly decreased ((80.93±6.69) mm vs. (63.73±6.31) mm, P<0.05), brain natriuretic peptide (BNP), total bilirubin and creatinine were also significantly decreased ((3 544.85±1 723.77) ng/L vs. (770.80±406.39) ng/L; (21.28±10.51) μmol/L vs. (17.39±7.68) μmol/L; (95.82±34.88) μmol/L vs. (77.32±43.81) μmol/L; P<0.05) at 1 week after device implantation. All patients in this group were in NYHA class Ⅳ before implantation, and 9 patients could recover to NYHA class Ⅲ, 3 to class Ⅱ, and 3 to class Ⅰ at 1 month after operation. All patients recovered to class Ⅰ-Ⅱ at 6 months after operation. The 6-minute walk distance, total quality of life and visual analogue scale were significantly increased and improved at 1 month after implantation compared with those before operation (P<0.05). All patients were implanted with EVAHEART I at speeds between 1 700-1 950 rpm, flow rates between 3.2-4.5 L/min, power consumption of 3-9 W. The 1-year, 2-year, and 3-year survival rates were 100%, 87%, and 80%, respectively. Three patients died of multiple organ failure at 412, 610, and 872 d after surgery, respectively. During long-term device carrying, 3 patients developed drive cable infection on 170, 220, and 475 d after surgery, respectively, and were cured by dressing change. One patient underwent heart transplantation at 155 d after surgery due to bacteremia. Three patients developed transient ischemic attack and 1 patient developed hemorrhagic stroke events, all cured without sequelae. Conclusion: EVAHEART I implantable left heart assist system can effectively treat critically ill patients with end-stage heart failure, can be carried for long-term life and significantly improve the survival rate, with clear clinical efficacy.
Humans ; Male ; Adult ; Middle Aged ; Female ; Heart Failure/complications* ; Follow-Up Studies ; Retrospective Studies ; Heart-Assist Devices ; Quality of Life

Objective: To investigate the diagnostic efficiency and clinical application value of an artificial intelligence-assisted diagnosis model based on a three-dimensional convolutional neural network (3D CNN) on echocardiographic videos of patients with hypertensive heart disease, chronic renal failure (CRF) and hypothyroidism with cardiac involvement. Methods: This study is a retrospective study. The patients with hypertensive heart disease, CRF and hypothyroidism with cardiac involvement, who admitted in Henan Provincial People's Hospital from April 2019 to October 2021, were enrolled. Patients were divided into hypertension group, CRF group, and hypothyroidism group. Additionally, a simple random sampling method was used to select control healthy individuals, who underwent physical examination at the same period. The echocardiographic video data of enrolled participants were analyzed. The video data in each group was divided into a training set and an independent testing set in a ratio of 5 to 1. The temporal and spatial characteristics of videos were extracted using an inflated 3D convolutional network (I3D). The artificial intelligence assisted diagnosis model was trained and tested. There was no case overlapped between the training and validation sets. A model was established according to cases or videos based on video data from 3 different views (single apical four chamber (A4C) view, single parasternal left ventricular long-axis (PLAX) view and all views). The statistical analysis of diagnostic performance was completed to calculate sensitivity, specificity and area under the ROC curve (AUC). The time required for the artificial intelligence and ultrasound physicians to process cases was compared. Results: A total of 730 subjects aged (41.9±12.7) years were enrolled, including 362 males (49.6%), and 17 703 videos were collected. There were 212 cases in the hypertensive group, 210 cases in the CRF group, 105 cases in the hypothyroidism group, and 203 cases in the normal control group. The diagnostic performance of the model predicted by cases based on single PLAX view and all views data was excellent: (1) in the hypertensive group, the sensitivity, specificity and AUC of models based on all views data were 97%, 89% and 0.93, respectively, while those of models based on a single PLAX view were 94%, 95%, and 0.94, respectively; (2) in the CRF group, the sensitivity, specificity and AUC of models based on all views data were 97%, 95% and 0.96, respectively, while those of models based on a single PLAX view were 97%, 89%, and 0.93, respectively; (3) in the hypothyroidism group, the sensitivity, specificity and AUC of models based on all views data were 64%, 100% and 0.82, respectively, while those of models based on a single PLAX view were 82%, 89%, and 0.86, respectively. The time required for the 3D CNN model to measure and analyze the echocardiographic videos of each subject was significantly shorter than that for the ultrasound physicians ((23.96±6.65)s vs. (958.25±266.17)s, P<0.001). Conclusions: The artificial intelligence assisted diagnosis model based on 3D CNN can extract the dynamic temporal and spatial characteristics of echocardiographic videos jointly, and quickly and efficiently identify hypertensive heart disease and cardiac changes caused by CRF and hypothyroidism.
Male ; Humans ; Artificial Intelligence ; Retrospective Studies ; Echocardiography/methods* ; Heart Diseases ; Hypertension ; Hypothyroidism

Humans ; Cardiac Resynchronization Therapy ; Cardiac Pacing, Artificial ; Heart Ventricles ; Heart Failure/therapy* ; Treatment Outcome ; Electrocardiography ; Ventricular Function, Left

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
Humans ; Artificial Intelligence ; Deep Learning ; Heart Failure/diagnosis* ; Machine Learning ; Diagnostic Imaging