Precision medicine in human heart modeling : Perspectives, challenges, and opportunities

A hybrid approach to heart disease prediction using a fractional-order mathematical model and machine learning algorithm

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of more accurate and efficient diagnostic tools. This study presents a hybrid approach to heart disease prediction, combining fractional-order dynamics with decision tree algorithms and an interactive graphical user interface (GUI). Fractional-order models allow for a more elaborate representation of the complex physiological processes involved in heart disease, including factors such as cholesterol levels, blood pressure, inflammation, and plaque buildup. By integrating decision trees, a machine learning (ML) method known for its interpretability and efficiency in classification tasks, this approach enhances predictive accuracy. The use of an interactive GUI further enables healthcare professionals to visualize and interact with the model, providing real-time insights into patient risk profiles. The model's fractional-order differential equations (FDEs) account for varying rates of progression in different health parameters, offering a dynamic view of heart disease risk. Comprehensive simulations demonstrate the efficacy of the model, which outperforms traditional prediction models in terms of both accuracy and usability. The hybrid framework is intended to serve as a robust tool for clinicians, offering an innovative combination of advanced mathematical modeling and user-friendly machine-learning techniques for heart disease prediction. Our findings show that the decision tree classifier performed well, with 93% accuracy, 95% precision, 90% recall, and an F1-score of 0.92. The model handled non-linear relationships and missing data effectively, achieving an ROC-AUC score of 0.99. Key correlations, such as between ST depression and exercise-induced angina, were identified. Fractional-order simulations revealed how cholesterol, blood pressure, and other factors influenced heart disease risk, reinforcing clinical links through numerical simulations.

A comparative study of lumped heart models for personalized medicine through sensitivity and identifiability analysis

Numerical modelling of the cardiovascular system is becoming an increasingly accepted tool for clinical applications, with the ultimate goal of personalized medicine. Lumped parameter models are attractive due to their low computational cost but often do not directly incorporate physical properties, thus requiring calibration to (often sparse) clinical data. Furthermore there exists a trade-off between physiological relevance and model complexity, making the choice of cardiac model non-trivial. For two established cardiac chamber models embedded in a haemodynamics whole circulation model, we perform sensitivity and identifiability analyses to examine the possibility of finding a unique subset of important parameters with varying levels of clinically measurable data, thereby examining their applicability in personalized medicine. To provide a concrete clinical context, the case of treatment planning for a young pulmonary arterial hypertension patient is considered. The methodology is however relevant for other pathophysiologies. The results suggest that the single-fibre model, although a priori more complex than the time-varying elastance model, is more amenable for patient-specific modelling. This was found for representing the patient state from clinical data, defining parameter ranges for sensitivity analysis (SA), and in the results of the identifiability analysis. SA also revealed the most influential parameters, which for the right (respectively left) heart chambers, mostly affect the haemodynamics of these chambers and the pulmonary (respectively systemic) circulation but also the ones of the left (respectively right) side. This highlights the importance of studying the whole circulation, especially in diseases traditionally thought to affect only one side. KEY POINTS: Two established cardiac chamber computational models are compared in the context of pulmonary hypertension, but the methodology holds for other pathophysiologies. Comprehensive patient-specific data, accurate parameter ranges and thorough model validation are essential to enhance parameter identifiability, improve model personalization and eventually lead to better prediction of haemodynamic changes after clinical intervention. Due to its inherent mathematical constraints and physiologically interpretable input parameters, the single-fibre model is easier to make patient-specific and more accurately captures the non-linear dynamics of ventricular pressure-volume loops than the simpler time-varying elastance model, making it more suitable for personalized cardiac simulations. Accurate representation of both the left and right heart chambers in cardiac modelling is important, as our results suggest that parameters of each side impact the haemodynamics of the other circulatory side, making it worthwhile to measure the characteristics of both the right and left chambers, even if the disease emerges from one side.

Features, functions, and associated diseases of visceral and ectopic fat: a comprehensive review

Obesity is a complex, chronic, and recurrent disease marked by abnormal or excessive fat accumulation that poses significant health risks. The distribution of body fat, especially ectopic fat deposition, plays a crucial role in the development of chronic metabolic diseases. Under normal conditions, fatty acids are primarily stored in subcutaneous adipose tissue; however, excessive intake can lead to fat accumulation in visceral adipose tissue and ectopic sites, including the pancreas, heart, and muscle. This redistribution is associated with disruptions in energy metabolism, inflammation, and insulin resistance, impairing organ function and raising the risk of cardiovascular disease, diabetes, and fatty liver. This review explores the roles of visceral and ectopic fat in the development of insulin resistance and related diseases such as type 2 diabetes and metabolic dysfunction-associated steatotic liver disease. Specifically, we examine the structure and characteristics of different fat types, their associations with disease, and the underlying pathogenic mechanisms. Future strategies for managing obesity-related diseases may include lifestyle modifications, surgical interventions, and emerging medications that target lipid metabolism and energy regulation, aiming to improve patient outcomes.

Quantifying anatomically-based in-silico electrocardiogram variability for cardiac digital twins

Human cardiac Cardiac digital twins (CDTs) are digital replicas of patient hearts, designed to match clinical observations precisely. The electro-cardiogram (ECG), as the most common non-invasive electrophysiology (EP) measurement, has been recently successfully employed for calibrating CDT. However, ECG-based calibration methods often fail to account for the inherent uncertainties in clinical data acquisition and CDT anatomical generation workflows. As a result, discrepancies inevitably arise between the actual physical and simulated patient EP and ECG. In this study, we aim to qualitatively and quantitatively analyze the impact of these uncertainties on ECG morphology and diagnostic markers, and therefore to assess the reliability of ECG-based CDT calibration. We analyze residual beat-to-beat variability in ECG recordings obtained from three datasets, including healthy subjects and patients treated for ventricular tachycardia and atrial fibrillation. Using a biophysically detailed and anatomically accurate computational model of whole-heart EP combined with a detailed torso model calibrated to closely replicate measured ECG signals, we vary anatomical factors (heart location, orientation, size), heterogeneity in electrical conductivities in the heart and torso, and electrode placements across ECG leads to assess their qualitative impact on ECG morphology. Our study demonstrates that diagnostically relevant ECG features and overall morphology remain close to the ground through ECG independently of the investigated uncertainties. This resilience is consistent with the narrow distribution of ECG due to residual beat-to-beat variability observed in both healthy subjects and patients. Overall, our results suggest that observation uncertainties do not impede an accurate calibration of the CDT.

Computer-generated Clinical Decision-making in the Treatment of Pulmonary Atresia with Intact Ventricular Septum

PURPOSE

Pulmonary atresia with intact ventricular septum is a multifactorial disease requiring complex surgeries. The treatment route is determined based on the right ventricle (RV) size, tricuspid annulus size and coronary circulation dependency of RV. Since multiple parameters influence the post-operative success, a personalized decision-making based on computed hemodynamics is hypothesized to improve the treatment efficacy.

METHODS

A lumped parameter cardiovascular model is developed to calculate the hemodynamics of virtual patients which are generated by statistical distribution of circulation parameters. Four cohorts each with 30 digital patients are grouped based on RV size. For each patient, biventricular and one-and-half ventricle (1.5 V) repair were applied in silico and assessed via pressure, flow and saturations computed for every organ bed.

RESULTS

Biventricular and 1.5 V repair yield significant increase in the pulmonary flow and oxygen saturation for all patients compared to the pre-operative state (p-values < 0.001). Approximately 30% of generated patients failed to meet the sufficient saturation and flow following biventricular repair and were directed to 1.5 V repair. However, 14% of these 1.5 V repair patients failed post-operatively, requiring Fontan completion. Based on the pre-determined hemodynamics criteria, this study implies that patients having RV sizes larger than 22 ml/m2 are likely to undergo successful biventricular repair.

CONCLUSION

Pending further clinical trials, computational pre-interventional planning has the potential to screen patients that would not optimally fit to the traditional pathway prior to in vivo execution by providing personalized hemodynamic outcome. Statistical approach allows in silico clinical trials, useful for diseases with low patient numbers.

Homogenized multiscale modelling of an electrically active double poroelastic material representing the myocardium

In this work, we present the derivation of a novel model for the myocardium that incorporates the underlying poroelastic nature of the material constituents as well as the electrical conductivity. The myocardium has a microstructure consisting of a poroelastic extracellular matrix with embedded poroelastic myocytes, i.e. a double poroelastic material. Due to the sharp length scale separation that exists between the microscale, where the individual myocytes are clearly resolved from the surrounding matrix, and the length of the entire heart muscle, we can apply the asymptotic homogenization technique. The novel PDE model accounts for the difference in the electric potentials, elastic properties as well as the differences in the hydraulic conductivities at different points in the microstructure. The differences in these properties are encoded in the coefficients and are to be computed by solving differential cell problems arising when applying the asymptotic homogenization technique. We present a numerical analysis of the obtained Biot's modulus, Young's moduli as well as shears and the effective electrical activity. By investigating the poroelastic and electrical nature of the myocardium in one model, we can understand how the differences in elastic displacements between the extracellular matrix and the myocytes affect mechanotransduction and the influence of disease.

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered

Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross procedure, using neural networks to discover the most suitable material model. The Ross procedure is a complex cardiac surgery where the patient's own pulmonary valve is used to replace the diseased aortic valve. Ensuring the successful long-term outcomes of this intervention requires a detailed understanding of the mechanical properties of pulmonary tissue. Constitutive artificial neural networks offer a novel approach to capture such complex stress-strain relationships. Here, we design and train different constitutive neural networks to characterize the hyperelastic, anisotropic behavior of the main pulmonary artery. Informed by experimental biaxial testing data under various axial-circumferential loading ratios, these networks autonomously discover the inherent material behavior, without the limitations of predefined mathematical models. We regularize the model discovery using cross-sample feature selection and explore its sensitivity to the collagen fiber distribution. Strikingly, we uniformly discover an isotropic exponential first-invariant term and an anisotropic quadratic fifth-invariant term. We show that constitutive models with both these terms can reliably predict arterial responses under diverse loading conditions. Our results provide crucial improvements in experimental data agreement, and enhance our understanding into the biomechanical properties of pulmonary tissue. The model outcomes can be used in a variety of computational frameworks of autograft adaptation, ultimately improving the surgical outcomes after the Ross procedure.

Characterizing variability in passive myocardial stiffness in healthy human left ventricles using personalized MRI and finite element modeling

Abnormal passive stiffness of the heart muscle (myocardium) is evident in the pathophysiology of several cardiovascular diseases, making it an important indicator of heart health. Recent advancements in cardiac imaging and biophysical modeling now enable more effective evaluation of this biomarker. Estimating passive myocardial stiffness can be accomplished through an MRI-based approach that requires comprehensive subject-specific input data. This includes the gross cardiac geometry (e.g. from conventional cine imaging), regional diastolic kinematics (e.g. from tagged MRI), microstructural configuration (e.g. from diffusion tensor imaging), and ventricular diastolic pressure, whether invasively measured or non-invasively estimated. Despite the progress in cardiac biomechanics simulations, developing a framework to integrate multiphase and multimodal cardiac MRI data for estimating passive myocardial stiffness has remained a challenge. Moreover, the sensitivity of estimated passive myocardial stiffness to input data has not been fully explored. This study aims to: (1) develop a framework for integrating subject-specific in vivo MRI data into in silico left ventricular finite element models to estimate passive myocardial stiffness, (2) apply the framework to estimate the passive myocardial stiffness of multiple healthy subjects under assumed filling pressure, and (3) assess the sensitivity of these estimates to loading conditions and myofiber orientations. This work contributes toward the establishment of a range of reference values for material parameters of passive myocardium in healthy human subjects. Notably, in this study, beat-to-beat variation in left ventricular end-diastolic pressure was found to have a greater influence on passive myocardial material parameter estimation than variation in fiber orientation.

Digital health in cardiovascular medicine: An overview of key applications and clinical impact by the Portuguese Society of Cardiology Study Group on Digital Health

Digital health interventions including telehealth, mobile health, artificial intelligence, big data, robotics, extended reality, computational and high-fidelity bench simulations are an integral part of the path toward precision medicine. Current applications encompass risk factor modification, chronic disease management, clinical decision support, diagnostics interpretation, preprocedural planning, evidence generation, education, and training. Despite the acknowledged potential, their development and implementation have faced several challenges and constraints, meaning few digital health tools have reached daily clinical practice. As a result, the Portuguese Society of Cardiology Study Group on Digital Health set out to outline the main digital health applications, address some of the roadblocks hampering large-scale deployment, and discuss future directions in support of cardiovascular health at large.

Assessing post-TAVR cardiac conduction abnormalities risk using an electromechanically coupled beating heart

Transcatheter aortic valve replacement (TAVR) has rapidly displaced surgical aortic valve replacement (SAVR). However, certain post-TAVR complications persist, with cardiac conduction abnormalities (CCA) being one of the major ones. The elevated pressure exerted by the TAVR stent onto the conduction fibers situated between the aortic annulus and the His bundle, in proximity to the atrioventricular (AV) node, may disrupt the cardiac conduction leading to the emergence of CCA. In this study, an in silico framework was developed to assess the CCA risk, incorporating the effect of a dynamic beating heart and preprocedural parameters such as implantation depth and preexisting cardiac asynchrony in the new onset of post-TAVR CCA. A self-expandable TAVR device deployment was simulated inside an electromechanically coupled beating heart model in five patient scenarios, including three implantation depths and two preexisting cardiac asynchronies: (i) a right bundle branch block (RBBB) and (ii) a left bundle branch block (LBBB). Subsequently, several biomechanical parameters were analyzed to assess the post-TAVR CCA risk. The results manifested a lower cumulative contact pressure on the conduction fibers following TAVR for aortic deployment (0.018 MPa) compared to nominal condition (0.29 MPa) and ventricular deployment (0.52 MPa). Notably, the preexisting RBBB demonstrated a higher cumulative contact pressure (0.34 MPa) compared to the nominal condition and preexisting LBBB (0.25 MPa). Deeper implantation and preexisting RBBB cause higher stresses and contact pressure on the conduction fibers leading to an increased risk of post-TAVR CCA. Conversely, implantation above the MS landmark and preexisting LBBB reduces the risk.

Phase stabilization with motion compensated diffusion weighted imaging

PURPOSE

Diffusion encoding gradient waveforms can impart intra-voxel and inter-voxel dephasing owing to bulk motion, limiting achievable signal-to-noise and complicating multishot acquisitions. In this study, we characterize improvements in phase consistency via gradient moment nulling of diffusion encoding waveforms.

METHODS

Healthy volunteers received neuro (N = 10 $$ N=10 $$ ) and cardiac (N = 10 $$ N=10 $$ ) MRI. Three gradient moment nulling levels were evaluated: compensation for position (M 0 $$ {M}_0 $$ ), position + velocity (M 1 $$ {M}_1 $$ ), and position + velocity + acceleration (M 1 + M 2 $$ {M}_1+{M}_2 $$ ). Three experiments were completed: (Exp-1) Fixed Trigger Delay Neuro DWI; (Exp-2) Mixed Trigger Delay Neuro DWI; and (Exp-3) Fixed Trigger Delay Cardiac DWI. Significant differences (p < 0 . 05 $$ p<0.05 $$ ) of the temporal phase SD between repeated acquisitions and the spatial phase gradient across a given image were assessed.

RESULTS

M 0 $$ {M}_0 $$ moment nulling was a reference for all measures. In Exp-1, temporal phase SD forG z $$ {G}_z $$ diffusion encoding was significantly reduced withM 1 $$ {M}_1 $$ (35% of t-tests) andM 1 + M 2 $$ {M}_1+{M}_2 $$ (68% of t-tests). The spatial phase gradient was reduced in 23% of t-tests forM 1 $$ {M}_1 $$ and 2% of cases forM 1 + M 2 $$ {M}_1+{M}_2 $$ . In Exp-2, temporal phase SD significantly decreased withM 1 + M 2 $$ {M}_1+{M}_2 $$ gradient moment nulling only forG z $$ {G}_z $$ (83% of t-tests), but spatial phase gradient significantly decreased with onlyM 1 $$ {M}_1 $$ (50% of t-tests). In Exp-3,M 1 + M 2 $$ {M}_1+{M}_2 $$ gradient moment nulling significantly reduced temporal phase SD and spatial phase gradients (100% of t-tests), resulting in less signal attenuation and more accurate ADCs.

CONCLUSION

We characterized gradient moment nulling phase consistency for DWI. Using M1 for neuroimaging and M1 + M2 for cardiac imaging minimized temporal phase SDs and spatial phase gradients.

Effect of excitation sequence of myocardial contraction on the mechanical response of the left ventricle

In the past two decades there has been rapid development in the field of computational cardiac models. These have included either (i) mechanical models that assumed simultaneous myocardial activation, or (ii) electromechanical models that assumed time-varying myocardial activation. The influence of these modelling assumptions of myocardial activation on clinically relevant metrics, like myocardial strain, commonly used for validation of cardiac models has yet to be systematically examined, leading to uncertainty over their influence on the predictions of these models. This study examined the effects of simultaneous (mechanical), uniform endocardial, 3-patch endocardial (simulating the fascicles of the His-Purkinje system) and 1-patch endocardial (simulating the atrioventricular node) excitation sequences on the mechanical response of a synthetic human left ventricular model. The influence of the duration of the activation and time-to-peak contraction was also investigated. The electromechanical and mechanical models produced different strain distributions in early systole. However, these differences decayed as systole progressed. Using the same activation duration (74 ms) the average peak-systolic circumferential strain difference between the models was 0.65±0.37 %. A slightly prolonged activation duration (134 ms) resulted in no substantial difference increase (0.76±0.47 %). Differences up to 3.5 % were observed for prolonged activation durations (200 ms). Endocardial excitation produced non-physiological cumulative activation time distributions compared to the other models. Septal 1-patch excitation resulted in early systolic strain response that resembled pathological left bundle branch block. Decreased time-to-peak contraction exaggerated the effects of electrophysiology. The study found that excitation sequence minimally affects strain distributions at peak systole for physiological and even slightly pathological activation durations. However, electromechanical models with (patho)physiologically informed activation sequences are important for the accurate prediction of early systolic and pathological late systolic responses.

Automated cell tracking using 3D nnUnet and Light Sheet Microscopy to quantify regional deformation in zebrafish

Light Sheet Microscopy (LSM) in conjunction with embryonic zebrafish, is rapidly advancing three-dimensional, in vivo characterization of myocardial contractility. Preclinical cardiac deformation imaging is predominantly restricted to a low-order dimensionality image space (2i) or suffers from poor reproducibility. In this regard, LSM has enabled high throughput, non-invasive 4i (3d+time) characterization of dynamic organogenesis within the transparent zebrafish model. More importantly, LSM offers cellular resolution across large imaging Field-of-Views at millisecond camera frame rates, enabling single cell localization for global cardiac deformation analysis. However, manual labeling of cells within multilayered tissue is a time-consuming task and requires substantial expertise. In this study, we applied the 3i nnU-Net with Linear Assignment Problem (LAP) framework for automated segmentation and tracking of myocardial cells. Using binarized labels from the neural network, we quantified myocardial deformation of the zebrafish ventricle across 4-6 days post fertilization (dpf). Our study offers tremendous promise for developing highly scalable and disease-specific biomechanical quantification of myocardial microstructures.

MV-GNN: Generation of continuous geometric representations of mitral valve motion from 3D+t echocardiography

We present a geometric deep-learning method for reconstructing a temporally continuous mitral valve surface mesh from 3D transesophageal echocardiography sequences. Our approach features a supervised end-to-end deep learning architecture that combines a convolutional neural network-based voxel encoder and decoder with a graph neural network-based multi-resolution mesh decoder, all trained on sparse landmark annotations. Key elements of our methodology include a tube-shaped prototype mesh with labeled vertices, a specialized loss function to preserve the known inlet and outlet, and a rigid alignment system for anatomical landmarks. A custom term in the loss function prevents self-intersecting geometries within the valve mesh, promoting point correspondence and facilitating a continuous representation of valve anatomy over time. An ablation study evaluates the impact of different loss term configurations on model performance, highlighting the effectiveness of each individual loss term. Our Mitral Valve Graph Neural Network (MV-GNN) outperforms existing deep-learning methods on most distance metrics for the annulus and leaflets. The continuous valve motion representations generated by our approach (3D+t) exhibit distance measures comparable to our 3D solution, demonstrating its potential for analyzing mitral valve dynamics and enhancing personalized simulations for hemodynamic assessment and therapy planning.

Ring only repair of bileaflet mitral valve prolapse with mitral regurgitation: Insights from computational modeling

This study evaluates the efficacy of annuloplasty repair as a standalone procedure for treating bileaflet mitral valve prolapse with mitral regurgitation (MR). Various flexible ring bands for MR of different severities were compared to assess their biomechanical impact and treatment outcomes. Computational beating heart models, based on the Living Heart Human Model, were utilized to simulate annuloplasty repairs. Repairs using bands of varying lengths were modeled on moderate and severe MR cases, considering bileaflet mitral valve prolapse. Key parameters, including regurgitant orifice area (ROA), prolapse severity, coaptation length, leaflet position, and deformation, were computed to compare conditions before and after implantation. Annuloplasty repairs effectively reduced the ROA in both moderate and severe MR cases, achieving complete sealing in selective instances. Additionally, annuloplasty repair corrected bileaflet prolapse, with prolapse severity decreasing as the annular size increased. Successful coaptation was indicated by the expansion of each leaflet's contact area distribution and percentage in contact with the opposing leaflet. The risk of systolic anterior motion, that may obstruct the left ventricular outflow tract, was minimized, as the anterior leaflet was directed towards the posterior position. In conclusion, annuloplasty repair alone can effectively treat MR when an appropriate band length is selected. It facilitates a significant reduction in ROA, correction of bileaflet prolapse, and improvement in leaflet coaptation. These findings have important clinical implications, potentially offering a less complex surgical treatment avenue and reducing complications in the management of MR.

Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images

The study of physiology demonstrates that the form (shape) of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical research. Statistical shape modeling (SSM) is a widely used tool for quantitative analysis of forms of anatomies, aiding in characterizing and identifying differences within a population of subjects. Despite its utility, the conventional SSM construction pipeline is often complex and time-consuming. Additionally, reliance on linearity assumptions further limits the model from capturing clinically relevant variations. Recent advancements in deep learning solutions enable the direct inference of SSM from unsegmented medical images, streamlining the process and improving accessibility. However, the new methods of SSM from images do not adequately account for situations where the imaging data quality is poor or where only sparse information is available. Moreover, quantifying aleatoric uncertainty, which represents inherent data variability, is crucial in deploying deep learning for clinical tasks to ensure reliable model predictions and robust decision-making, especially in challenging imaging conditions. Therefore, we propose SPI-CorrNet, a unified model that predicts 3D correspondences from sparse imaging data. It leverages a teacher network to regularize feature learning and quantifies data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variances. Experiments on the LGE MRI left atrium dataset and Abdomen CT-1K liver datasets demonstrate that our technique enhances the accuracy and robustness of sparse image-driven SSM.

A universal material model subroutine for soft matter systems

Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. Today's finite element analysis packages come with a set of pre-programmed material models, which may exhibit restricted validity in capturing the intricate mechanical behavior of these materials. Regrettably, incorporating a modified or novel material model in a finite element analysis package requires non-trivial in-depth knowledge of tensor algebra, continuum mechanics, and computer programming, making it a complex task that is prone to human error. Here we design a universal material subroutine, which automates the integration of novel constitutive models of varying complexity in non-linear finite element packages, with no additional analytical derivations and algorithmic implementations. We demonstrate the versatility of our approach to seamlessly integrate innovative constitutive models from the material point to the structural level through a variety of soft matter case studies: a frontal impact to the brain; reconstructive surgery of the scalp; diastolic loading of arteries and the human heart; and the dynamic closing of the tricuspid valve. Our universal material subroutine empowers all users, not solely experts, to conduct reliable engineering analysis of soft matter systems. We envision that this framework will become an indispensable instrument for continued innovation and discovery within the soft matter community at large.

Unlocking the potentials of digital twins for optimal healthcare delivery in Africa

Advances in big data analysis, the Internet of Things and simulation technology have led to a surge in interest in digital twin technology, which creates virtual clones of physical entities across several industries. The technological revolution with digital twins, incorporating Internet of Things, big data analysis and simulation technologies, holds the potential for predictive insights, real-time monitoring and increased operational efficiency across the healthcare industry. This paper explores the potential of digital twins to improve healthcare delivery and health outcomes in Africa. It examines their applications in various health sectors, explores their feasibility and highlights the potential challenges associated with their implementation while proposing sustainable recommendations.

Bioinspired chitosan based functionalization of biomedical implant surfaces for enhanced hemocompatibility, antioxidation and anticoagulation potential: an in silico and in vitro study

Endowing implanted biomaterials with better hemocompatibility, anticoagulation, antioxidant and antiplatelet adhesion is necessary because of their potential to trigger activation of multiple reactive mechanisms including coagulation cascade and potentially causing serious adverse clinical events like late thrombosis. Active ingredients from natural sources including Foeniculum vulgare, Angelica sinensis, and Cinnamomum verum have the ability to inhibit the coagulation cascade and thrombus formation around biomedical implants. These properties are of interest for the development of a novel drug for biomedical implants to potentially solve the current blood clotting and coagulation problems which lead to stent thrombosis. The objective of this study was to incorporate different anticoagulants from natural sources into a degradable matrix of chitosan with varying concentrations ranging from 5% to 15% and a composite containing all three drugs. The presence of anticoagulant constituents was identified using GC-MS. Subsequently, all the compositions were characterized principally by using Fourier transform infrared spectroscopy and scanning electron microscopy while the drug release profile was determined using UV-spectrometry for a 30 days immersion period. The results indicated an initial burst release which was subsequently followed by the sustained release pattern. Compared to heparin loaded chitosan, DPPH and hemolysis tests revealed better blood compatibility of natural drug loaded films. Moreover, the anticoagulation activity of natural drugs was equivalent to the heparin loaded film; however, through docking, the mechanism of inhibition of the coagulation cascade of the novel drug was found to be through blocking the extrinsic pathway. The study suggested that the proposed drug composite expresses an optimum composition which may be a practicable and appropriate candidate for biomedical implant coatings.

Digital twinning of cardiac electrophysiology for congenital heart disease

In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient

Single ventricle patients, including those with hypoplastic left heart syndrome (HLHS), typically undergo three palliative heart surgeries culminating in the Fontan procedure. HLHS is associated with high rates of morbidity and mortality, and many patients develop arrhythmias, electrical dyssynchrony, and eventually ventricular failure. However, the correlation between ventricular enlargement and electrical dysfunction in HLHS physiology remains poorly understood. Here we characterize the relationship between growth and electrophysiology in HLHS using computational modeling. We integrate a personalized finite element model, a volumetric growth model, and a personalized electrophysiology model to perform controlled in silico experiments. We show that right ventricle enlargement negatively affects QRS duration and interventricular dyssynchrony. Conversely, left ventricle enlargement can partially compensate for this dyssynchrony. These findings have potential implications on our understanding of the origins of electrical dyssynchrony and, ultimately, the treatment of HLHS patients.

Modeling the Electrical Activity of the Heart via Transfer Functions and Genetic Algorithms

Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA). The transfer function parameters were fine-tuned using the GA, with clinical ECG records serving as reference signals. The proposed model, which is based on polynomials and delays, approximates a real ECG with a root-mean-square error of 4.7% and an R2 value of 0.72. The model achieves the periodic nature of an ECG signal by using a single periodic impulse input. Its simplicity makes it possible to adjust waveform parameters with a predetermined understanding of their effects, which can be used to generate both arrhythmic patterns and healthy signals. This is a notable advantage over other models that are burdened by a large number of differential equations and many parameters.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Tetralogy of Fallot regurgitation energetics and kinetics: an intracardiac flow analysis of the right ventricle using computational fluid dynamics

Repaired Tetralogy of Fallot (rTOF) patients suffer from pulmonary regurgitation and may require pulmonary valve replacement (PVR). Cardiac magnetic resonance imaging (cMRI) guides therapy, but conventional measurements do not quantify the intracardiac flow effects from pulmonary regurgitation or PVR. This study investigates intracardiac flow parameters of the right ventricle (RV) of rTOF by computational fluid dynamics (CFD). cMRI of rTOF patients and controls were retrospectively included. Feature-tracking captured RV endocardial contours from long-axis/short-axis cine. Ventricular motion was reconstructed via diffeomorphic mapping, serving as domain boundary for CFD simulations. Vorticity (1/s), viscous energy loss (ELoss, mJ/L) and turbulent kinetic energy (TKE, mJ/L) were quantified in RV outflow tract (RVOT) and RV inflow. These parameters were normalized against total RV kinetic energy (KE) and RV inflow vorticity to derive dimensionless metrics. Vorticity contours by Q-criterion were qualitatively compared. rTOF patients (n = 15) had mean regurgitant fraction 38 ± 12% and RV size 162 ± 35 mL/m2. Compared to controls (n = 12), rTOF had increased RVOT vorticity (142.6 ± 75.6/s vs. 40.4 ± 11.8/s, p < 0.0001), Eloss (55.6 ± 42.5 vs. 5.2 ± 4.4 mJ/L, p = 0.0004), and TKE (5.7 ± 5.9 vs. 0.84 ± 0.46 mJ/L, p = 0.0003). After PVR, there was decrease in normalized RVOT Eloss/TKE (p = 0.0009, p = 0.029) and increase in normalized tricuspid inflow vorticity/KE (p = 0.0136, p = 0.043), corresponding to reorganization of the "donut"-shaped tricuspid ring-vortex. The intracardiac flow in rTOF patients can be simulated to determine the impact of PVR and improve the clinical indications guided by cardiac imaging.

Sex-specific cardiovascular risk factors in the UK Biobank

The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of women compared to that of men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual's risk of developing cardiovascular disease based on the age, sex, cholesterol levels, blood pressure, diabetes status, and the smoking status. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular diseases. The UK Biobank is a large database that includes traditional risk factors and tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here, we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score and quantify the underdiagnosis of women compared to that of men. Strikingly, for a first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2× and 1.4× more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that out of the top 10 features across three sexes and four disease categories, traditional Framingham factors made up between 40% and 50%; electrocardiogram, 30%-33%; pulse wave analysis, 13%-23%; and magnetic resonance imaging and carotid ultrasound, 0%-10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management. Our analysis pipeline and trained classifiers are freely available at https://github.com/LivingMatterLab/CardiovascularDiseaseClassification.

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor.

A universal biventricular coordinate system incorporating valve annuli: Validation in congenital heart disease

Universal coordinate systems have been proposed to facilitate anatomic registration between three-dimensional images, data and models of the ventricles of the heart. However, current universal ventricular coordinate systems do not account for the outflow tracts and valve annuli where the anatomy is complex. Here we propose an extension to the 'Cobiveco' biventricular coordinate system that also accounts for the intervalvular bridges of the base and provides a tool for anatomically consistent registration between widely varying biventricular shapes. CobivecoX uses a novel algorithm to separate intervalvular bridges and assign new coordinates, including an inflow-outflow coordinate, to describe local positions in these regions uniquely and consistently. Anatomic consistency of registration was validated using curated three-dimensional biventricular shape models derived from cardiac MRI measurements in normal hearts and hearts from patients with congenital heart diseases. This new method allows the advantages of universal cardiac coordinates to be used for three-dimensional ventricular imaging data and models that include the left and right ventricular outflow tracts and valve annuli.

Assessing Post-TAVR Cardiac Conduction Abnormalities Risk Using a Digital Twin of a Beating Heart

Transcatheter aortic valve replacement (TAVR) has rapidly displaced surgical aortic valve replacement (SAVR). However, certain post-TAVR complications persist, with cardiac conduction abnormalities (CCA) being one of the major ones. The elevated pressure exerted by the TAVR stent onto the conduction fibers situated between the aortic annulus and the His bundle, in proximity to the atrioventricular (AV) node, may disrupt the cardiac conduction leading to the emergence of CCA. In his study, an in-silico framework was developed to assess the CCA risk, incorporating the effect of a dynamic beating heart and pre-procedural parameters such as implantation depth and preexisting cardiac asynchrony in the new onset of post-TAVR CCA. A self-expandable TAVR device deployment was simulated inside an electro-mechanically coupled beating heart model in five patient scenarios, including three implantation depths, and two preexisting cardiac asynchronies: (i) a right bundle branch block (RBBB) and (ii) a left bundle branch block (LBBB). Subsequently, several biomechanical parameters were analyzed to assess the post-TAVR CCA risk. The results manifested a lower cumulative contact pressure on the conduction fibers following TAVR for aortic deployment (0.018 MPa) compared to baseline (0.29 MPa) and ventricular deployment (0.52 MPa). Notably, the preexisting RBBB demonstrated a higher cumulative contact pressure (0.34 MPa) compared to the baseline and preexisting LBBB (0.25 MPa). Deeper implantation and preexisting RBBB cause higher stresses and contact pressure on the conduction fibers leading to an increased risk of post-TAVR CCA. Conversely, implantation above the MS landmark and preexisting LBBB reduces the risk.

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equations, thus demanding extensive computational resources. In contrast, data-driven approaches leverage deep learning algorithms to describe system evolution in low-dimensional spaces. We introduce an architecture, termed Latent Dynamics Network, capable of uncovering low-dimensional intrinsic dynamics in potentially non-Markovian systems. Latent Dynamics Networks automatically discover a low-dimensional manifold while learning the system dynamics, eliminating the need for training an auto-encoder and avoiding operations in the high-dimensional space. They predict the evolution, even in time-extrapolation scenarios, of space-dependent fields without relying on predetermined grids, thus enabling weight-sharing across query-points. Lightweight and easy-to-train, Latent Dynamics Networks demonstrate superior accuracy (normalized error 5 times smaller) in highly-nonlinear problems with significantly fewer trainable parameters (more than 10 times fewer) compared to state-of-the-art methods.

Heart Failure: Recent Advances and Breakthroughs

Heart failure (HF) is a common clinical condition encountered in various healthcare settings with a vast socioeconomic impact. Recent advancements in pharmacotherapy have led to the evolution of novel therapeutic agents with a decrease in hospitalization and mortality rates in HF with reduced left ventricular ejection fraction (HFrEF). Lately, the introduction of artificial intelligence (AI) to construct decision-making models for the early detection of HF has played a vital role in optimizing cardiovascular disease outcomes. In this review, we examine the newer therapies and evidence behind goal-directed medical therapy (GDMT) for managing HF. We also explore the application of AI and machine learning (ML) in HF, including early diagnosis and risk stratification for HFrEF.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

Endoscopic versus laparoscopic bariatric procedures: A computational biomechanical study through a patient-specific approach

BACKGROUND AND OBJECTIVES

Within the framework of computational biomechanics, finite element models of the gastric district could be seen as a potential clinical tool not only to study the effects apported by bariatric surgery, but also to compare different surgical techniques such as the new emerging Endoscopic Sleeve Gastroplasty (ESG) with respect to well-established ones (such as the Laparoscopic Sleeve Gastrectomy, LSG).

METHODS

This work realized a fully computational comparison between the outcomes obtained from 10 patient-specific stomach models, which were used to simulate ESG, and the complementary results obtained from models representing the post-LSG of the same subjects. Specifically, once the ESG was simulated, a mechanical stimulus was applied by increasing an intragastric pressure up to a maximum of 5 kPa, in order to replicate the process of food intake, as well as for post-LSG models.

RESULTS

Results revealed non negligible differences between the techniques also within the same subject. In particular, not only LSG could lead to a greater reduction in the stomach volume (about 77 % at baseline, which is strictly linked to weight loss), but also influence the gastric distension (12 % less than pre-operative models). On the contrary, if ESG would be performed, a more similar pre-operative mechanical stimulation of the gastric walls may be seen (difference of about 1 %), thus preserving the mechanosensation, but the detriment of the volume reduction (about 56 % at baseline, and even decreases with increasing pressure). Moreover, since results suggested ESG may be more influenced by the pre-operative gastric cavity than LSG, a predictive model was proposed to support the surgical planning and the estimation of the volume reduction after ESG.

CONCLUSIONS

ESG and LSG have substantial differences in their protocols and post-surgical effects. This work pointed out that variations between the two procedures may be observed also from a computational point of view, especially when including patient-specific geometries. These insights support gastric modelling as a valuable tool to evaluate, design and critically compare emerging bariatric surgical procedures, not only from empirical aspects and clinical outcomes, but also from a mechanical point of view.

Branched Latent Neural Maps

We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 hours on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10 - 4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.

A multiscale finite element model of left ventricular mechanics incorporating baroreflex regulation

Cardiovascular function is regulated by a short-term hemodynamic baroreflex loop, which tries to maintain arterial pressure at a normal level. In this study, we present a new multiscale model of the cardiovascular system named MyoFE. This framework integrates a mechanistic model of contraction at the myosin level into a finite-element-based model of the left ventricle pumping blood through the systemic circulation. The model is coupled with a closed-loop feedback control of arterial pressure inspired by a baroreflex algorithm previously published by our team. The reflex loop mimics the afferent neuron pathway via a normalized signal derived from arterial pressure. The efferent pathway is represented by a kinetic model that simulates the net result of neural processing in the medulla and cell-level responses to autonomic drive. The baroreflex control algorithm modulates parameters such as heart rate and vascular tone of vessels in the lumped-parameter model of systemic circulation. In addition, it spatially modulates intracellular Ca2+ dynamics and molecular-level function of both the thick and the thin myofilaments in the left ventricle. Our study demonstrates that the baroreflex algorithm can maintain arterial pressure in the presence of perturbations such as acute cases of altered aortic resistance, mitral regurgitation, and myocardial infarction. The capabilities of this new multiscale model will be utilized in future research related to computational investigations of growth and remodeling.

Review on Emerging Therapeutic Strategies for Managing Cardiovascular Disease

Cardiovascular disease (CVD) remains a foremost global health concern, necessitating ongoing exploration of innovative therapeutic strategies. This review surveys the latest developments in cardiovascular therapeutics, offering a comprehensive overview of emerging approaches poised to transform disease management. The examination begins by elucidating the current epidemiological landscape of CVD and the economic challenges it poses to healthcare systems. It proceeds to scrutinize the limitations of traditional therapies, emphasizing the need for progressive interventions. The core focus is on novel pharmacological interventions, including advancements in drug development, targeted therapies, and repurposing existing medications. The burgeoning field of gene therapy and its potential in addressing genetic predispositions to cardiovascular disorders are explored, alongside the integration of artificial intelligence and machine learning in risk assessment and treatment optimization. Non-pharmacological interventions take center stage, with an exploration of digital health technologies, wearable devices, and telemedicine as transformative tools in CVD management. Regenerative medicine and stem cell therapies, offering promises of tissue repair and functional recovery, are investigated for their potential impact on cardiac health. This review also delves into the interplay of lifestyle modifications, diet, exercise, and behavioral changes, emphasizing their pivotal role in cardiovascular health and disease prevention. As precision medicine gains prominence, this synthesis of emerging therapeutic modalities aims to guide clinicians and researchers in navigating the dynamic landscape of cardiovascular disease management, fostering a collective effort to alleviate the global burden of CVD and promote a healthier future.

Current progress toward isogeometric modeling of the heart biophysics

In this paper, we review a powerful methodology to solve complex numerical simulations, known as isogeometric analysis, with a focus on applications to the biophysical modeling of the heart. We focus on the hemodynamics, modeling of the valves, cardiac tissue mechanics, and on the simulation of medical devices and treatments. For every topic, we provide an overview of the methods employed to solve the specific numerical issue entailed by the simulation. We try to cover the complete process, starting from the creation of the geometrical model up to the analysis and post-processing, highlighting the advantages and disadvantages of the methodology.

Digital twinning of cardiac electrophysiology for congenital heart disease

In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in pediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and utilizing rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in pediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

BACKGROUND

Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.

RESULTS

This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.

CONCLUSIONS

[Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Digital twin for healthcare systems

Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.

Mathematical modeling of active contraction of the human cardiac myocyte: A review

Background and objective

In this present research paper, a mathematical model has been developed to study myocyte contraction in the human cardiac muscle, using the Land model. Different parts of the human heart with a focus on the composition of the myocyte cells have been explored numerically to enabling us to determine the interaction of various parameters in the heart muscle. The main objective of the work is to direct the study of the Land model, which has been exploited to simulate the contraction of real human myocytes.

Methods

Mathematical models has been developed based on the Hill model and Huxley model. Myocyte contraction for different scenarios, such as in isometric tension and isotonic tension have been studied.

Results

It is found that increase in stretch, the peak active tension increases, in line with well-established length-dependent tension generation. Five parameters are selected: [Ca2+]T50, Tref, TRPN50, β0, and β1, which have been varied in between the range of -50%-100%, to examine the isometric effects of each parameter on the behavior of the tension developed in the intact myocyte cells, with the most sensitive parameter being [Ca2+]T50.

Conclusion

In conclusion, it is found that the Land model provides a good platform for the analysis of the active contraction of the human cardiac myocyte.

Literature review of digital twin in healthcare

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

Computational fluid dynamics as supporting technology for coronary artery disease diagnosis and treatment: an international survey

BACKGROUND

Computational fluid dynamics (CFD) is emerging as an effective technology able to improve procedural outcomes and enhance clinical decision-making in patients with coronary artery disease (CAD). The present study aims to assess the state of knowledge, use and clinical acceptability of CFD in the diagnosis and treatment of CAD.

METHODS

We realized a 20-questions international, anonymous, cross-sectional survey to cardiologists to test their knowledge and confidence on CFD as a technology applied to patients suffering from CAD. Responses were recorded between May 18, 2022, and June 12, 2022.

RESULTS

A total of 466 interventional cardiologists (mean age 48.4 ± 8.3 years, males 362), from 42 different countries completed the survey, for a response rate of 45.9%. Of these, 66.6% declared to be familiar with the term CFD, especially for optimization of existing interventional techniques (16.1%) and assessment of hemodynamic quantities related with CAD (13.7%). About 30% of respondents correctly answered to the questions exploring their knowledge on the pathophysiological role of some CFD-derived quantities such as wall shear stress and helical flow in coronary arteries. Among respondents, 85.9% would consider patient-specific CFD-based analysis in daily interventional practice while 94.2% declared to be interested in receiving a brief foundation course on the basic CFD principles. Finally, 87.7% of respondents declared to be interested in a cath-lab software able to conduct affordable CFD-based analyses at the point-of-care.

CONCLUSIONS

Interventional cardiologists reported to be profoundly interested in adopting CFD simulations as a technology supporting decision making in the treatment of CAD in daily practice.

Smoothed finite element methods in simulation of active contraction of myocardial tissue samples

In modelling and simulation of cardiac mechanics, tetrahedral meshes are often used due to the easy availability of efficient meshing algorithms. This is beneficial in particular when complex geometries such as cardiac structures are considered. The gold standard in simulating the cardiac cycle is to solve the mechanical balance equations with the finite element method (FEM). However, using linear shape functions in the FEM in combination with nearly-incompressible material models is known to produce overly stiff approximations, whereas higher order elements are computationally more expensive. To overcome these problems, smoothed finite element methods (S-FEMs) have been proposed by Liu and co-workers. So far, S-FEMs in 3D have been utilised only in simulations of passive mechanics. In the present work, different S-FEMs are for the first time used for simulation of an active cardiac contraction on three-dimensional myocardial tissue samples. Further, node-based S-FEM (NS-FEM), face-based S-FEM (FS-FEM) and selective FS/NS-FEM are for the first time implemented as user subroutine in the commercial software Abaqus. Our results confirm that all S-FEMs perform softer than linear FEM and volumetric locking is reduced. The FS/NS-FEM produces solutions with the relative error in maximum displacement and rotation being less than 5% with respect to the reference solution obtained by the quadratic FEM for all considered mesh sizes, although linear shape functions are used. We therefore conclude that in particular FS/NS-FEM is an efficient and accurate numerical method in the simulation of an active cardiac muscle contraction.

The potential of the Medical Digital Twin in diabetes management: a review

Diabetes is a chronic prevalent disease that must be managed to improve the patient's quality of life. However, the limited healthcare management resources compared to the large diabetes mellitus (DM) population are an obstacle that needs modern information technology to improve. Digital twin (DT) is a relatively new approach that has emerged as a viable tool in several sectors of healthcare, and there have been some publications on DT in disease management. The systematic summary of the use of DTs and its potential applications in DM is less reported. In this review, we summarized the key techniques of DTs, proposed the potentials of DTs in DM management from different aspects, and discussed the concerns of this novel technique in DM management.

The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases

Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.

GPU accelerated digital twins of the human heart open new routes for cardiovascular research

The recruitment of patients for rare or complex cardiovascular diseases is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative. In this paper we present an unprecedented cardiovascular computer model which, relying on the latest GPU-acceleration technologies, replicates the full multi-physics dynamics of the human heart within a few hours per heartbeat. This opens the way to extensive simulation campaigns to study the response of synthetic cohorts of patients to cardiovascular disorders, novel prosthetic devices or surgical procedures. As a proof-of-concept we show the results obtained for left bundle branch block disorder and the subsequent cardiac resynchronization obtained by pacemaker implantation. The in-silico results closely match those obtained in clinical practice, confirming the reliability of the method. This innovative approach makes possible a systematic use of digital twins in cardiovascular research, thus reducing the need of real patients with their economical and ethical implications. This study is a major step towards in-silico clinical trials in the era of digital medicine.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

BACKGROUND AND OBJECTIVE

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA.

METHODS

Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated.

RESULTS

Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors.

CONCLUSIONS

We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.