The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Machine learning-driven predictions and interventions for cardiovascular occlusions.

BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in improving patient outcomes. The specific disease, cardiovascular occlusions, has been chosen for the study due to the significant impact it has on public health. Cardiovascular diseases are a leading cause of mortality globally, and occlusions, which are blockages in the blood vessels, are a critical factor contributing to these conditions. OBJECTIVE: By focusing on cardiovascular occlusions, the study aims to leverage machine learning to improve the prediction and management of these events, potentially helping to reduce the incidence of heart attacks, strokes, and other related health issues. The use of machine learning in this context offers the promise of developing more accurate and timely interventions, thus improving patient outcomes. METHODS: We analyze diverse datasets to assess the efficacy of various machine learning algorithms in predicting heart attacks and strokes, comparing their performance to pinpoint the most accurate and reliable models. Additionally, we classify individuals by their predicted risk levels and examine key features that correlate with the incidence of cardiovascular events. The PyCaret machine learning library's Classification Module was key in developing predictive models which were evaluated with stratified cross-validation for reliable performance estimates. RESULTS: Our findings suggest that machine learning can significantly improve the prediction accuracy for heart attacks and strokes, facilitating earlier and more precise interventions. We also discuss the integration of machine learning models into clinical practice, addressing potential challenges and the need for healthcare professionals to interpret and apply these predictions effectively. CONCLUSIONS: The use of machine learning for risk stratification and the identification of modifiable factors may empower preemptive approaches to cardiovascular care, ultimately aiming to reduce the occurrence of life-threatening events and improve long-term patient health trajectories.

Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress.

Epilepsy is one of the most common neurological disorders globally, affecting about 50 million people, with nearly 80% of those affected residing in low- and middle-income countries. It is characterized by recurrent seizures that result from abnormal electrical brain activity, with seizures varying widely in manifestation. The exploration of the biomechanical effects that seizures have on brain dynamics and stress levels is relevant for the development of more effective treatments and protective strategies. This study uses a blend of experimental data and computational simulations to assess the brain's physical response during seizures, particularly focusing on the behavior of cerebrospinal fluid and the resulting mechanical stresses on different brain regions. Notable findings show increases in stress, predominantly in the posterior gyri and brainstem, during seizures and an evidence of brain displacement relative to the skull. These observations suggest a dynamic and complex interaction between the brain and skull, with maximum shear stress regions demonstrating the limited yet essential protective role of the CSF. By providing a deeper understanding of the mechanical changes occurring during seizures, this research supports the goal of advancing diagnostic tools, informing more targeted treatment interventions, and guiding the creation of customized therapeutic strategies to enhance neurological care and protect against the adverse effects of seizures.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.

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.

Understanding Traumatic Brain Injuries in Military Personnel: Investigating the Dynamic Interplay of the Cerebrospinal Fluid and Brain During Blasts.

Background It is estimated that around 450,000 traumatic brain injury cases have occurred in the 21st century with possible under-reporting. Computational simulations are increasingly used to study the pathophysiology of traumatic brain injuries among US military personnel. This approach allows for investigation without ethical concerns surrounding live subject testing. Methodology The pertinent data on head acceleration is applied to a detailed 3D model of a patient-specific head, which encompasses all significant components of the brain and its surrounding fluid. The use of finite element analysis and smoothed-particle hydrodynamics serves to replicate the interaction between these elements during discharge through simulation of their fluid-structure dynamics. Results The stress levels of the brain are assessed at varying time intervals subsequent to the explosion. The regions where there is an intersection between the skull and brain are observed, along with the predominant orientations in which displacement of the brain occurs resulting in a brain injury. Conclusions It has been determined that the cerebrospinal fluid is inadequate in preventing brain damage caused by multiple abrupt directional shifts of the head. Accordingly, additional research must be undertaken to enhance our comprehension of the injury mechanisms linked with consecutive changes in acceleration impacting the head.

Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps.

(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Haemodynamic changes in visceral hybrid repairs of type III and type V thoracoabdominal aortic aneurysms.

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.

Modeling Dynamics of the Cardiovascular System Using Fluid-Structure Interaction Methods.

Using fluid-structure interaction algorithms to simulate the human circulatory system is an innovative approach that can provide valuable insights into cardiovascular dynamics. Fluid-structure interaction algorithms enable us to couple simulations of blood flow and mechanical responses of the blood vessels while taking into account interactions between fluid dynamics and structural behaviors of vessel walls, heart walls, or valves. In the context of the human circulatory system, these algorithms offer a more comprehensive representation by considering the complex interplay between blood flow and the elasticity of blood vessels. Algorithms that simulate fluid flow dynamics and the resulting forces exerted on vessel walls can capture phenomena such as wall deformation, arterial compliance, and the propagation of pressure waves throughout the cardiovascular system. These models enhance the understanding of vasculature properties in human anatomy. The utilization of fluid-structure interaction methods in combination with medical imaging can generate patient-specific models for individual patients to facilitate the process of devising treatment plans. This review evaluates current applications and implications of fluid-structure interaction algorithms with respect to the vasculature, while considering their potential role as a guidance tool for intervention procedures.

Fluid-structure interaction analysis of amniotic fluid with fetus and placenta inside uterus exposed to military blasts.

INTRODUCTION: Pregnancy-related trauma is one of the leading causes of morbidity and mortality in pregnant women and fetuses. The fetal response to injury is largely dependent on the timing of fetal presentation and the underlying pathophysiology of the trauma. The optimal management of pregnant patients who have suffered an obstetric emergency depends on clinical assessment and understanding of the placental implantation process, which can be difficult to perform during an emergency. Understanding the mechanisms of traumatic injuries to the fetus is crucial for developing next-generation protective devices. METHODS: This study aimed to investigate the effect of amniotic fluid on mine blast on the uterus, fetus, and placenta via computational analysis. Finite element models were developed to analyze the effects of explosion forces on the uterus, fetus, and placenta, based on cadaveric data obtained from the literature. This study uses computational fluid-structure interaction simulations to study the effect of external loading on the fetus submerged in amniotic fluid inside of the uterus. RESULTS: Computational fluid-structure interaction simulations are used to study the effect of external loading on the fetus/placenta submerged in amniotic fluid inside the uterus. Cushioning function of the amniotic fluid on the fetus and placenta is demonstrated. The mechanism of traumatic injuries to the fetus/placenta is shown. DISCUSSION: The intention of this research is to understand the cushioning function of the amniotic fluid on the fetus. Further, it is important to make use of this knowledge in order to ensure the safety of pregnant women and their fetuses.

Assessment of Abdominal Constrictor's Forces for Informing Computational Models of Orthostatic Hypotension.

Orthostatic hypotension is defined as a sudden drop in blood pressure upon standing from a sitting or supine position. The prevalence of this condition increases exponentially with age. Nonpharmacological treatments are always the first step in the management of this condition, such as the use of an abdominal constriction belt to optimize the blood volume in the abdomen. A multitude of clinical trials have shown the efficacy of elastic abdominal compression as well as compression using an inflatable bladder; however, there are currently few accessible consumer products that can provide abdominal compression by using an inflatable bladder that ensures the correct amount of pressure is being exerted on the subject. This study serves to quantitatively analyze forces exerted in inflatable abdominal binders, a novel treatment that fits the criterion for a first-line intervention for orthostatic hypotension. Quantitative values aim to indicate both the anatomic regions of the body subjected to the highest pressure by abdominal binding. Quantitative values will also create a model that can correlate the amount of compression on the subject with varying levels of pressure in the inflatable bladder. Inflatable binders of varying levels of inflation are used and localized pressure values are recorded at 5 different vertical points along the abdomen in the midsternal line and midclavicular line, at the locations of the splanchnic veins. These findings indicate both the differences in the compressive force applied through elastic and inflatable binding, as well the regions on the abdomen subject to the highest force load during compression by an abdominal binder. A medical manikin called the iStan Manikin was used to collect data. The pressure values on a manikin were sensed by the JUZO pressure monitor, a special device created for the purpose of measuring the force under compressive garments. The pressure inside the inflatable bladder was extrapolated from a pressure gauge and the pressure was recorded at different degrees of inflation of the belt (mmHG) along two different areas of the abdomen, the midsternal line and the midclavicular line, to discern differences in force exerted on the patient (mmHG). Computational studies on the data from the JUZO pressure monitor as well as the data from the pressure gauge on the inflatable bladder allow us to create a model that can correlate the amount of pressure in the inflatable bladder to the amount of pressure exerted on the belt, thus making sure that the patient is not being harmed by the compressive force. The results of our study indicate that there is no significant difference between the pressures exerted on the midsternal and midclavicular lines of the body by the abdominal binder and that no significant difference exists between the external pressure measured by the inflatable belt and the pressure sensed on the human body by the JUZO sensor; however, we were able to extrapolate an equation that can tell the user the amount of pressure that is actually being exerted on them based on the pressure in the inflatable bladder as recorded by the gauge.

Clinical Impact of Computational Heart Valve Models.

This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and replacement have long-term stability and biocompatibility issues, highlighting the need for a more robust method for resolving valvular disease. For example, while fluid-structure interaction analyses are still scarcely utilized to study aortic valves, computational fluid dynamics is used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta. It has been analyzed that computational flow dynamic analyses can be integrated with other methods to create a superior, more compatible method of understanding risk and compatibility.

Addressing Discrepancies between Experimental and Computational Procedures.

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.

Fluid-Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics.

Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid-structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid-structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid-structure interactions in complex biological systems.

Computational Biology: A New Frontier in Applied Biology.

All living things are related to one another [...].

Assessing Forces Exerted on Horses Using Varying Riding Crops.

There is a multitude of whips and riding crops. It is assumed that the whip in Thoroughbred racing must be padded and designed to be energy-absorbing. The new whips have a cushion made of softer material to be presumably more ethical when used on the horses. This study quantifies the forces exerted on a flat target plate using three different riding crops. The goal is to comparatively determine which one is less likely to leave a mark on the equine skin when the same bending level of the crop cores is achieved. Counterintuitively, it is shown that the riding crop even when its popper is made of softer material can still exert forces larger than the traditional crops with stiffer poppers made of leather. The resulting force depends on the combination of the core and the popper, but the crop core appears to have a more significant role than the crop popper.

Effect of Edge-to-Edge Mitral Valve Repair on Chordal Strain: Fluid-Structure Interaction Simulations.

Edge-to-edge repair for mitral valve regurgitation is being increasingly performed in high-surgical risk patients using minimally invasive mitral clipping devices. Known procedural complications include chordal rupture and mitral leaflet perforation. Hence, it is important to quantitatively evaluate the effect of edge-to-edge repair on chordal integrity. in this study, we employ a computational mitral valve model to simulate functional mitral regurgitation (FMR) by creating papillary muscle displacement. Edge-to-edge repair is then modeled by simulated coaptation of the mid portion of the mitral leaflets. in the setting of simulated FMR, edge-to-edge repair was shown to sustain low regurgitant orifice area, until a two fold increase in the inter-papillary muscle distance as compared to the normal mitral valve. Strain in the chordae was evaluated near the papillary muscles and the leaflets. Following edge-to-edge repair, strain near the papillary muscles did not significantly change relative to the unrepaired valve, while strain near the leaflets increased significantly relative to the unrepaired valve. These data demonstrate the potential for computational simulations to aid in the pre-procedural evaluation of possible complications such as chordal rupture and leaflet perforation following percutaneous edge-to-edge repair.

Development of a Computational Method for Simulating Tricuspid Valve Dynamics.

Computational modeling can be used to improve understanding of tricuspid valve (TV) biomechanics and supplement knowledge gained from benchtop and large animal experiments. The aim of this study was to develop a computational model of the TV using high resolution micro-computed tomography (µCT) imaging and fluid-structure interaction simulations. A three-dimensional TV model, incorporating detailed leaflet and chordal geometries, was reconstructed from µCT images of an excised porcine TV obtained under diastolic conditions. The leaflets were described using non-linear stress-strain relations and chordal properties were iteratively adjusted until valve closure was obtained. The leaflet coaptation zone obtained from simulation of valve closure was validated against µCT images of the TV captured at peak systole. The computational model was then used to simulate a regurgitant TV morphology and investigate changes in closure dynamics. Overall, the mean stresses in the leaflet belly region and the chordae tendinae of the regurgitant TV were 7% and 3% higher than the same regions of the normal TV. The maximum principal strain in the leaflet belly of the regurgitant TV was also 9% higher than the same regions of the normal TV. It is anticipated that this computational model can be used in future studies for further understanding of TV biomechanics and associated percutaneous repairs.

Fluid-structure interaction analysis of cerebrospinal fluid with a comprehensive head model subject to a rapid acceleration and deceleration.

PRIMARY OBJECTIVE: Closed brain injuries are a common danger in contact sports and motorized vehicular collisions. Mild closed brain injuries, such as concussions, are not easily visualized by computed imaging or scans. Having a comprehensive head/brain model and using fluid-structure interaction (FSI) simulations enable us to see the exact movement of the cerebrospinal fluid (CSF) under such conditions and to identify the areas of brain most affected. RESEARCH DESIGN: The presented work is based on the first FSI model capable of simulating the interaction between the CSF flow and brain. METHODS AND PROCEDURES: FSI analysis combining smoothed-particle hydrodynamics and high-order finite-element method is used. MAIN OUTCOMES AND RESULTS: The interaction between the CSF and brain under rapid acceleration and deceleration is demonstrated. The cushioning effect of the fluid and its effect on brain are shown. CONCLUSIONS: The capability to locate areas (down to the exact gyri and sulci) of the brain the most affected under given loading conditions, and therefore assess the possible damage to the brain and consequently predict the symptoms, is shown.

Computational Fluid Dynamics Assessment Associated with Transcatheter Heart Valve Prostheses: A Position Paper of the ISO Working Group.

The governing international standard for the development of prosthetic heart valves is International Organization for Standardization (ISO) 5840. This standard requires the assessment of the thrombus potential of transcatheter heart valve substitutes using an integrated thrombus evaluation. Besides experimental flow field assessment and ex vivo flow testing, computational fluid dynamics is a critical component of this integrated approach. This position paper is intended to provide and discuss best practices for the setup of a computational model, numerical solving, post-processing, data evaluation and reporting, as it relates to transcatheter heart valve substitutes. This paper is not intended to be a review of current computational technology; instead, it represents the position of the ISO working group consisting of experts from academia and industry with regards to considerations for computational fluid dynamic assessment of transcatheter heart valve substitutes.

Fluid-structure interaction and structural analyses using a comprehensive mitral valve model with 3D chordal structure.

Over the years, three-dimensional models of the mitral valve have generally been organized around a simplified anatomy. Leaflets have been typically modeled as membranes, tethered to discrete chordae typically modeled as one-dimensional, non-linear cables. Yet, recent, high-resolution medical images have revealed that there is no clear boundary between the chordae and the leaflets. In fact, the mitral valve has been revealed to be more of a webbed structure whose architecture is continuous with the chordae and their extensions into the leaflets. Such detailed images can serve as the basis of anatomically accurate, subject-specific models, wherein the entire valve is modeled with solid elements that more faithfully represent the chordae, the leaflets, and the transition between the two. These models have the potential to enhance our understanding of mitral valve mechanics and to re-examine the role of the mitral valve chordae, which heretofore have been considered to be 'invisible' to the fluid and to be of secondary importance to the leaflets. However, these new models also require a rethinking of modeling assumptions. In this study, we examine the conventional practice of loading the leaflets only and not the chordae in order to study the structural response of the mitral valve apparatus. Specifically, we demonstrate that fully resolved 3D models of the mitral valve require a fluid-structure interaction analysis to correctly load the valve even in the case of quasi-static mechanics. While a fluid-structure interaction mode is still more computationally expensive than a structural-only model, we also show that advances in GPU computing have made such models tractable. Copyright © 2016 John Wiley & Sons, Ltd.