Personalized cardiovascular intervention simulation system

Background This study proposes a series of geometry and physics modeling methods for personalized cardiovascular intervention procedures,which can be applied to a virtual endovascular simulator.Methods Based on personalized clinical computed tomography angiography(CTA)data,mesh models of the cardiovascular system were constructed semi-automatically.By coupling 4 D magnetic resonance imaging(MRI)sequences corresponding to a complete cardiac cycle with related physics models,a hybrid kinetic model of the cardiovascular system was built to drive kinematics and dynamics simulation.On that basis,the surgical procedures related to intervention instruments were simulated using specially-designed physics models.These models can be solved in real-time;therefore,the complex interactions between blood vessels and instruments can be well simulated.Additionally,X-ray imaging simulation algorithms and realistic rendering algorithms for virtual intervention scenes are also proposed.In particular,instrument tracking hardware with haptic feedback was developed to serve as the interaction interface of real instruments and the virtual intervention system.Finally,a personalized cardiovascular intervention simulation system was developed by integrating the techniques mentioned above.Results This system supported instant modeling and simulation of personalized clinical data and significantly improved the visual and haptic immersions of vascular intervention simulation.Conclusions It can be used in teaching basic cardiology and effectively satisfying the demands of intervention training,personalized intervention planning,and rehearsing.

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation

Taking the Chinese city of Xiamen as an example,simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019(COVID-19)and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures.A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios.The comparison was conducted between simulated and real cases in Xiamen.A web interface with adjustable parameters,including choice of intervention measures,intervention weights,vaccination,and viral variants,was designed for users to run the simulation.The total case number was set as the outcome.The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set.Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model.The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200,which were 25 more days and 36 fewer cases than the real situation,respectively.Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people’s livelihood.