This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patie...This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.展开更多
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 person...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.展开更多
文摘This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.
基金the Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund(L 182016)Natural Science Foundation of China(61672077,61532002)Applied Basic Research Program of Qingdao(161013 xx).
文摘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.
