Explore the vast range of titles available.

Predictive modeling is a complex methodology that involves leveraging advanced mathematical and computational techniques to forecast future occurrences or outcomes. This tool has numerous applications in medicine, yet its full potential remains untapped within this field. Therefore, it is imperative to delve deeper into the benefits and drawbacks associated with utilizing predictive modeling in medicine for a more comprehensive understanding of how this approach may be effectively leveraged for improved patient care. When implemented successfully, predictive modeling has yielded impressive results across various medical specialities. From predicting disease progression to identifying high-risk patients who require early intervention, there are countless examples of successful implementations of this approach within healthcare settings worldwide. However, despite these successes, significant challenges remain for practitioners when applying predictive models to real-world scenarios. These issues include concerns about data quality and availability as well as navigating regulatory requirements surrounding the use of sensitive patient information—all factors that can impede progress toward realizing the true potential impact of predictive modeling on improving health outcomes.

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Background The growing prevalence of mental health conditions, worsened by the COVID-19 pandemic, highlights the urgent need for enhanced psychiatric education. The distinctive nature of psychiatry– which is heavily centred on communication skills, interpersonal skills, and interviewing techniques– indicates a necessity for further research into the use of GenAI in psychiatric education. Objective Given GenAI has shown promising outcomes in medical education, this study aims to discuss the possible roles of GenAI in psychiatric education. Methods We conducted a scoping review to identify the role of GenAI in psychiatric education based on the educational framework of the Canadian Medical Education Directives for Specialists (CanMEDS). Results Of the 12,594 papers identified, five studies met the inclusion criteria, revealing key roles for GenAI in case-based learning, simulation, content synthesis, and assessments. Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy were highlighted. Conclusions Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy have been highlighted. This study contributes to understanding how GenAI can enhance psychiatric education and suggests future research directions to refine its use in training medical students and primary care physicians. GenAI has significant potential to address the growing demand for mental health professionals, provided its limitations are carefully managed.

Critical aortic stenosis (AS) of the fetal heart causes a drastic change in the cardiac biomechanical environment. Consequently, a substantial proportion of such cases will lead to a single-ventricular birth outcome. However, the biomechanics of the disease is not well understood. To address this, we performed Finite Element (FE) modelling of the healthy fetal left ventricle (LV) based on patient-specific 4D ultrasound imaging, and simulated various disease features observed in clinical fetal AS to understand their biomechanical impact. These features included aortic stenosis, mitral regurgitation (MR) and LV hypertrophy, reduced contractility, and increased myocardial stiffness. AS was found to elevate LV pressures and myocardial stresses, and depending on severity, can drastically decrease stroke volume and myocardial strains. These effects are moderated by MR. AS alone did not lead to MR velocities above 3 m/s unless LV hypertrophy was included, suggesting that hypertrophy may be involved in clinical cases with high MR velocities. LV hypertrophy substantially elevated LV pressure, valve flow velocities and stroke volume, while reducing LV contractility resulted in diminished LV pressure, stroke volume and wall strains. Typical extent of hypertrophy during fetal AS in the clinic, however, led to excessive LV pressure and valve velocity in the FE model, suggesting that reduced contractility is typically associated with hypertrophy. Increased LV passive stiffness, which might represent fibroelastosis, was found to have minimal impact on LV pressures, stroke volume, and wall strain. This suggested that fibroelastosis could be a by-product of the disease progression and does not significantly impede cardiac function. Our study demonstrates that FE modelling is a valuable tool for elucidating the biomechanics of congenital heart disease and can calculate parameters which are difficult to measure, such as intraventricular pressure and myocardial stresses.

The visceral hybrid procedure combining retrograde visceral bypass grafting and completion endovascular stent grafting is a feasible alternative to conventional open surgical or wholly endovascular repairs of thoracoabdominal aneurysms (TAAA). However, the wide variability in visceral hybrid configurations means that a priori prediction of surgical outcome based on haemodynamic flow profiles such as velocity pattern and wall shear stress post repair remain challenging. We sought to appraise the clinical relevance of computational fluid dynamics (CFD) analyses in the setting of visceral hybrid TAAA repairs. Two patients, one with a type III and the other with a type V TAAA, underwent successful elective and emergency visceral hybrid repairs, respectively. Flow patterns and haemodynamic parameters were analysed using reconstructed pre- and post-operative CT scans. Both type III and type V TAAAs showed highly disturbed flow patterns with varying helicity values preoperatively within their respective aneurysms. Low time-averaged wall shear stress (TAWSS) and high endothelial cell action potential (ECAP) and relative residence time (RRT) associated with thrombogenic susceptibility was observed in the posterior aspect of both TAAAs preoperatively. Despite differing bypass configurations in the elective and emergency repairs, both treatment options appear to improve haemodynamic performance compared to preoperative study. However, we observed reduced TAWSS in the right iliac artery (portending a theoretical risk of future graft and possibly limb thrombosis), after the elective type III visceral hybrid repair, but not the emergency type V repair. We surmise that this difference may be attributed to the higher neo-bifurcation of the aortic stent graft in the type III as compared to the type V repair. Our results demonstrate that CFD can be used in complicated visceral hybrid repair to yield potentially actionable predictive insights with implications on surveillance and enhanced post-operative management, even in patients with complicated geometrical bypass configurations.

BackgroundSome Congenital Heart Malformations develops because of cardiac abnormalities during mid-gestation, which prevents normal development for the rest of gestation to lead to the malformation at birth. An example is foetal critical aortic stenosis, where an outflow obstruction causes high left ventricle (LV) pressures, low myocardial strains, and severe mitral regurgitation (MR). These abnormal conditions cause hypoplastic left heart Syndrome (HLHS) by birth in most cases, and are thus evolving HLHS cases. In such cases, catheter-based foetal aortic balloon valvuloplasty in utero interventions was shown to be promising in relieving the abnormal biomechanics, significantly reducing chances of single ventricular birth. However, the biomechanical nature of stenosis and intervention remain poorly characterized, even though they both have significant biomechanical effects. Further, our ability to predict outcomes of disease or intervention is very limited. We hypothesize that advanced image-based biomechanical simulations can improve our understanding, and can be used as a tool to better predict intervention outcomes. Here, we present preliminary work towards testing this hypothesis.Methods4D echocardiography images of foetal hearts from both healthy and diseased (critical aortic stenosis) foetuses were analysed for numerical reconstruction of the LV and its motion, using validated motion tracking algorithms. Image-based patient specific Finite Element Modeling of the LV was conducted to determine the biomechanical effects of various individual features of foetal aortic stenosis. This model featured both active tension and passive stiffness of myocardium, spatially varying myofiber orientations, and a simplified Windkessel model to describe ventricular-vascular coupling. Image-based computational fluid dynamics modeling of the LV was also conducted to understand flow patterns and forces in the LV during disease, compared to healthy hearts.ResultsFetal aortic stenosis alone was found to elevate LV pressures by 10-20 mmHg, and could drastically decrease stroke volume and myocardial strain, depending on severity. These effects were moderated down by MR. Our modelling indicated that stenosis alone could not lead to regurgitation velocities as high as clinical observations, unless hypertrophic wall thickening occurred. Indeed, our clinical data showed approximately 105±37% wall thickening. Modelling further indicated that this typical extent of hypertrophy produced LV pressures much higher than clinical invasive measurements, suggesting that contractility also weakened. Fibroelastosis was tested in our model by increasing myocardial stiffness, but was found to be inconsequential to cardiac biomechanics and function, suggesting that the conventional belief that fibroelastosis causes dysfunction is not true, and that it could merely be a by-product of the disease. Flow Simulations showed that a fetal aortic stenosis caused fast and narrow inflow fluid jet that collided with the apex, and led to chaotic vorticity patterns in the LV, altered wall shear stresses patterns, and drastically increased energy losses and cardiac work done.ConclusionWe developed image-based simulation tools that can analyse the biomechanics and function of the foetal heart computationally. These simulations were able to provide insights into the disease conditions, and could thus be useful tools. Our future work is to use these simulations to predict the outcome of the foetal heart interventions.Conflict of InterestNone

To investigate local hemodynamic changes resulting from elevated intraocular pressure (IOP) in different vasculature networks using a computational fluid dynamics model based on 3D reconstructed confocal microscopic images. Three-dimensional rat retinal vasculature was reconstructed from confocal microscopy images using a 3D U-Net-based labeling technique, followed by manual correction. We conducted a computational fluid dynamics (CFD) analysis on different retinal vasculature networks derived from a single rat. Various venule and arteriole pressures were applied to mimic the effects of elevated intraocular pressure (IOP), a major glaucoma risk factor. An increase in IOP typically correlates with a decrease in venous pressure. We also varied the percentage of capillary dropout, simulating the loss of blood vessels within the capillary network, by reducing the volume of the normal capillary network by 10%, 30%, and 50%. Based on the output of the CFD analysis, we calculated velocity, wall shear stress (WSS), and pressure gradient for different vasculature densities. Arteriolar pressure, venular pressure, and capillary dropout appear to be important factors influencing wall shear stress in the rat capillary network. Our study revealed that the pressure gradient between arterioles and venules strongly affects the local wall shear stress distribution across the 3D retinal vasculature. Specifically, under a pressure gradient of 3,250 Pa, the wall shear stress was found to vary between 0 and 20 Pa, with the highest shear stress observed in the region of the superficial layer. Additionally, capillary dropout led to a 25% increase or decrease in wall shear stress in affected areas. The hemodynamic differences under various arteriole and venule pressures, along with different capillary dropout conditions, could help explain the development of various optic disorders, such as glaucoma, diabetic retinopathy, and retinal vein occlusion.

Heart failure is a global health concern with significant implications for healthcare systems. Left ventricular assist devices (LVADs) provide mechanical support for patients with severe heart failure. However, the placement of the LVAD outflow graft within the aorta has substantial implications for hemodynamics and can lead to aortic insufficiency during long-term support. This study employs computational fluid dynamics (CFD) simulations to investigate the impact of different LVAD outflow graft locations on aortic hemodynamics. The introduction of valve morphology within the aorta geometry allows for a more detailed analysis of hemodynamics at the aortic root. The results demonstrate that the formation of vortex rings and subsequent vortices during the high-velocity jet flow from the graft interacted with the aortic wall. Time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) indicate that modification of the outflow graft location changes mechanical states within the aortic wall and aortic valve. Among the studied geometric factors, both the height and inclination angle of the LVAD outflow graft are important in controlling retrograde flow to the aortic root, while the azimuthal angle primarily determines the rotational direction of blood flow in the aortic arch. Thus, precise positioning of the LVAD outflow graft emerges as a critical factor in optimizing patient outcomes by improving the hemodynamic environment.

Imaging subject-specific heart valve, a crucial step to its design, has experimental variables that if unaccounted for, may lead to erroneous computational analysis and geometric errors of the resulting model. Preparation methods are developed to mitigate some sources of the geometric error. However, the resulting 3D geometry often does not retain the original dimensions before excision. Inverse fluid–structure interaction analysis is used to analyze the resulting geometry and to assess the valve’s closure. Based on the resulting closure, it is determined if the geometry used can yield realistic results. If full closure is not reached, the geometry is adjusted adequately until closure is observed.

Thoracic endovascular aortic repair (TEVAR) has been introduced as a less invasive approach to the treatment of thoracic aortic aneurysm (TAA). However, the effectiveness of TEVAR in the treatment of TAA is often limited due to the complex anatomy of aortic arch. Flow preservation at the three supra-aortic branches further increases the overall technical difficulty. This study proposes a novel stent graft design with slit perforations that can positively alter the hemodynamics at the aortic arch while maintaining blood flow to supra-aortic branches. We carried out a computational fluid dynamic (CFD) analysis to evaluate flow characteristics near stented aortic arch in simplified TAA models, followed by in-vitro experiments using particle image velocimetry (PIV) in a mock circulatory loop. The hemodynamics result was studied in terms of time-averaged wall shear stress (TAWSS), oscillating shear index (OSI), and endothelial cell action potential (ECAP). The results showed that the stent graft with slit perforations can reduce the disturbed flow region considerably. Furthermore, the effect of the slits on flow preservation to the supra-aortic branches was simulated and compared with experimental results. The effectiveness of the stent graft with slit perforations in preserving flow to the branches was demonstrated by both simulated and experimental results. Low TAWSS and elevated ECAP were observed in the aortic arch aneurysm after the placement of the stent graft with slits, implying the potential of thrombus formation in the aneurysm. On the other hand, the effects of the stent grafts with full-slit design and half-slit design on the shear stress did not differ significantly. The present analysis indicated that not only could the stent graft with slit perforations shield the aneurysm from rupture, but also it resulted in a favorable environment for thrombus that can contribute to the shrinkage of the aneurysm.