Computational investigation of left ventricular hemodynamics following bioprosthetic aortic and mitral valve replacement.

The left ventricle of the heart is a fundamental structure in the human cardiac system that pumps oxygenated blood into the systemic circulation. Several valvular conditions can cause the aortic and mitral valves associated with the left ventricle to become severely diseased and require replacement. However, the clinical outcomes of such operations, specifically the postoperative ventricular hemodynamics of replacing both valves, are not well understood. This work uses computational fluid-structure interaction (FSI) to develop an improved understanding of this effect by modeling a left ventricle with the aortic and mitral valves replaced with bioprostheses. We use a hybrid Arbitrary Lagrangian-Eulerian/immersogeometric framework to accommodate the analysis of cardiac hemodynamics and heart valve structural mechanics in a moving fluid domain. The motion of the endocardium is obtained from a cardiac biomechanics simulation and provided as an input to the proposed numerical framework. The results from the simulations in this work indicate that the replacement of the native mitral valve with a tri-radially symmetric bioprosthesis dramatically changes the ventricular hemodynamics. Most significantly, the vortical motion in the left ventricle is found to reverse direction after mitral valve replacement. This study demonstrates that the proposed computational FSI framework is capable of simulating complex multiphysics problems and can provide an in-depth understanding of the cardiac mechanics.

Rapid B-rep model preprocessing for immersogeometric analysis using analytic surfaces.

Computational fluid dynamics (CFD) simulations of flow over complex objects have been performed traditionally using fluid-domain meshes that conform to the shape of the object. However, creating shape conforming meshes for complicated geometries like automobiles require extensive geometry preprocessing. This process is usually tedious and requires modifying the geometry, including specialized operations such as defeaturing and filling of small gaps. Hsu et al. (2016) developed a novel immersogeometric fluid-flow method that does not require the generation of a boundary-fitted mesh for the fluid domain. However, their method used the NURBS parameterization of the surfaces for generating the surface quadrature points to enforce the boundary conditions, which required the B-rep model to be converted completely to NURBS before analysis can be performed. This conversion usually leads to poorly parameterized NURBS surfaces and can lead to poorly trimmed or missing surface features. In addition, converting simple geometries such as cylinders to NURBS imposes a performance penalty since these geometries have to be dealt with as rational splines. As a result, the geometry has to be inspected again after conversion to ensure analysis compatibility and can increase the computational cost. In this work, we have extended the immersogeometric method to generate surface quadrature points directly using analytic surfaces. We have developed quadrature rules for all four kinds of analytic surfaces: planes, cones, spheres, and toroids. We have also developed methods for performing adaptive quadrature on trimmed analytic surfaces. Since analytic surfaces have frequently been used for constructing solid models, this method is also faster to generate quadrature points on real-world geometries than using only NURBS surfaces. To assess the accuracy of the proposed method, we perform simulations of a benchmark problem of flow over a torpedo shape made of analytic surfaces and compare those to immersogeometric simulations of the same model with NURBS surfaces. We also compare the results of our immersogeometric method with those obtained using boundary-fitted CFD of a tessellated torpedo shape, and quantities of interest such as drag coefficient are in good agreement. Finally, we demonstrate the effectiveness of our immersogeometric method for high-fidelity industrial scale simulations by performing an aerodynamic analysis of a truck that has a large percentage of analytic surfaces. Using analytic surfaces over NURBS avoids unnecessary surface type conversion and significantly reduces model-preprocessing time, while providing the same accuracy for the aerodynamic quantities of interest.

Biomechanics Simulations Using Cubic Hermite Meshes with Extraordinary Nodes for Isogeometric Cardiac Modeling.

Cubic Hermite hexahedral finite element meshes have some well-known advantages over linear tetrahedral finite element meshes in biomechanical and anatomic modeling using isogeometric analysis. These include faster convergence rates as well as the ability to easily model rule-based anatomic features such as cardiac fiber directions. However, it is not possible to create closed complex objects with only regular nodes; these objects require the presence of extraordinary nodes (nodes with 3 or >= 5 adjacent elements in 2D) in the mesh. The presence of extraordinary nodes requires new constraints on the derivatives of adjacent elements to maintain continuity. We have developed a new method that uses an ensemble coordinate frame at the nodes and a local-to-global mapping to maintain continuity. In this paper, we make use of this mapping to create cubic Hermite models of the human ventricles and a four-chamber heart. We also extend the methods to the finite element equations to perform biomechanics simulations using these meshes. The new methods are validated using simple test models and applied to anatomically accurate ventricular meshes with valve annuli to simulate complete cardiac cycle simulations.

Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions.

We evaluate different Neural Radiance Field (NeRF) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 min of training on the graphics processing unit, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4% in the average F1 score. This optimization process substantially enhances the speed and efficiency of 3D reconstruction using NeRFs. Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.

Cyber-agricultural systems for crop breeding and sustainable production.

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development.

Successful Cardiac Resynchronization Therapy Reduces Negative Septal Work in Patient-Specific Models of Dyssynchronous Heart Failure.

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

Novel role for vinculin in ventricular myocyte mechanics and dysfunction.

Vinculin (Vcl) plays a key structural role in ventricular myocytes that, when disrupted, can lead to contractile dysfunction and dilated cardiomyopathy. To investigate the role of Vcl in myocyte and myocardial function, cardiomyocyte-specific Vcl knockout mice (cVclKO) and littermate control wild-type mice were studied with transmission electron microscopy (TEM) and in vivo magnetic resonance imaging (MRI) tagging before the onset of global ventricular dysfunction. MRI revealed significantly decreased systolic strains transverse to the myofiber axis in vivo, but no changes along the muscle fibers or in fiber tension in papillary muscles from heterozygous global Vcl null mice. Myofilament lattice spacing from TEM was significantly greater in cVclKO versus wild-type hearts fixed in the unloaded state. AFM in Vcl heterozygous null mouse myocytes showed a significant decrease in membrane cortical stiffness. A multiscale computational model of ventricular mechanics incorporating cross-bridge geometry and lattice mechanics showed that increased transverse systolic stiffness due to increased lattice spacing may explain the systolic wall strains associated with Vcl deficiency, before the onset of ventricular dysfunction. Loss of cardiac myocyte Vcl may decrease systolic transverse strains in vivo by decreasing membrane cortical tension, which decreases transverse compression of the lattice thereby increasing interfilament spacing and stress transverse to the myofibers.

Myofiber prestretch magnitude determines regional systolic function during ectopic activation in the tachycardia-induced failing canine heart.

Electrical dyssynchrony leads to prestretch in late-activated regions and alters the sequence of mechanical contraction, although prestretch and its mechanisms are not well defined in the failing heart. We hypothesized that in heart failure, fiber prestretch magnitude increases with the amount of early-activated tissue and results in increased end-systolic strains, possibly due to length-dependent muscle properties. In five failing dog hearts with scars, three-dimensional strains were measured at the anterolateral left ventricle (LV). Prestretch magnitude was varied via ventricular pacing at increasing distances from the measurement site and was found to increase with activation time at various wall depths. At the subepicardium, prestretch magnitude positively correlated with the amount of early-activated tissue. At the subendocardium, local end-systolic strains (fiber shortening, radial wall thickening) increased proportionally to prestretch magnitude, resulting in greater mean strain values in late-activated compared with early-activated tissue. Increased fiber strains at end systole were accompanied by increases in preejection fiber strain, shortening duration, and the onset of fiber relengthening, which were all positively correlated with local activation time. In a dog-specific computational failing heart model, removal of length and velocity dependence on active fiber stress generation, both separately and together, alter the correlations between local electrical activation time and timing of fiber strains but do not primarily account for these relationships.

Optimization framework for patient-specific modeling under uncertainty.

Estimating a patient-specific computational model's parameters relies on data that is often unreliable and ill-suited for a deterministic approach. We develop an optimization-based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient-specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real-world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient-specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data.

A computational framework for transmission risk assessment of aerosolized particles in classrooms.

Infectious airborne diseases like the recent COVID-19 pandemic render confined spaces high-risk areas. However, in-person activities like teaching in classroom settings and government services are often expected to continue or restart quickly. It becomes important to evaluate the risk of airborne disease transmission while accounting for the physical presence of humans, furniture, and electronic equipment, as well as ventilation. Here, we present a computational framework and study based on detailed flow physics simulations that allow straightforward evaluation of various seating and operating scenarios to identify risk factors and assess the effectiveness of various mitigation strategies. These scenarios include seating arrangement changes, presence/absence of computer screens, ventilation rate changes, and presence/absence of mask-wearing. This approach democratizes risk assessment by automating a key bottleneck in simulation-based analysis-creating an adequately refined mesh around multiple complex geometries. Not surprisingly, we find that wearing masks (with at least 74% inward protection efficiency) significantly reduced transmission risk against unmasked and infected individuals. While the use of face masks is known to reduce the risk of transmission, we perform a systematic computational study of the transmission risk due to variations in room occupancy, seating layout and air change rates. In addition, our findings on the efficacy of face masks further support use of face masks. The availability of such an analysis approach will allow education administrators, government officials (courthouses, police stations), and hospital administrators to make informed decisions on seating arrangements and operating procedures. Supplementary Information: The online version contains supplementary material available at 10.1007/s00366-022-01773-9.

CyRSoXS: a GPU-accelerated virtual instrument for polarized resonant soft X-ray scattering.

Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework - called CyRSoXS (https://github.com/usnistgov/cyrsoxs) - is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument for operando analytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposing CyRSoXS to Python using Pybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.

GPU-Accelerated Collision Analysis of Vehicles in a Point Cloud Environment.

We present a GPU-accelerated collision detection method for the navigation of vehicles in enclosed spaces represented using large point clouds. Our approach takes a CAD model of a vehicle, converts it to a volumetric representation or voxels, and computes the collision of the voxels with a point cloud representing the environment to identify a suitable path for navigation. We perform adaptive and efficient collision of voxels with the point cloud without the need for mesh generation. We have developed a GPU-accelerated voxel Minkowski sum algorithm to perform a clearance analysis of the vehicle. Finally, we provide theoretical bounds for the accuracy of the collision and clearance analysis. Our GPU implementation is linked with Unreal Engine to provide flexibility in performing the analysis.

Deep Learning for Live Cell Shape Detection and Automated AFM Navigation.

Atomic force microscopy (AFM) provides a platform for high-resolution topographical imaging and the mechanical characterization of a wide range of samples, including live cells, proteins, and other biomolecules. AFM is also instrumental for measuring interaction forces and binding kinetics for protein-protein or receptor-ligand interactions on live cells at a single-molecule level. However, performing force measurements and high-resolution imaging with AFM and data analytics are time-consuming and require special skill sets and continuous human supervision. Recently, researchers have explored the applications of artificial intelligence (AI) and deep learning (DL) in the bioimaging field. However, the applications of AI to AFM operations for live-cell characterization are little-known. In this work, we implemented a DL framework to perform automatic sample selection based on the cell shape for AFM probe navigation during AFM biomechanical mapping. We also established a closed-loop scanner trajectory control for measuring multiple cell samples at high speed for automated navigation. With this, we achieved a 60× speed-up in AFM navigation and reduced the time involved in searching for the particular cell shape in a large sample. Our innovation directly applies to many bio-AFM applications with AI-guided intelligent automation through image data analysis together with smart navigation.

Polarized X-ray scattering measures molecular orientation in polymer-grafted nanoparticles.

Polymer chains are attached to nanoparticle surfaces for many purposes, including altering solubility, influencing aggregation, dispersion, and even tailoring immune responses in drug delivery. The most unique structural motif of polymer-grafted nanoparticles (PGNs) is the high-density region in the corona where polymer chains are stretched under significant confinement, but orientation of these chains has never been measured because conventional nanoscale-resolved measurements lack sensitivity to polymer orientation in amorphous regions. Here, we directly measure local chain orientation in polystyrene grafted gold nanoparticles using polarized resonant soft X-ray scattering (P-RSoXS). Using a computational scattering pattern simulation approach, we measure the thickness of the anisotropic region of the corona and extent of chain orientation within it. These results demonstrate the power of P-RSoXS to discover and quantify orientational aspects of structure in amorphous soft materials and provide a framework for applying this emerging technique to more complex, chemically heterogeneous systems in the future.

Performing efficient NURBS modeling operations on the GPU.

We present algorithms for evaluating and performing modeling operations on NURBS surfaces using the programmable fragment processor on the Graphics Processing Unit (GPU). We extend our GPU-based NURBS evaluator that evaluates NURBS surfaces to compute exact normals for either standard or rational B-spline surfaces for use in rendering and geometric modeling. We build on these calculations in our new GPU algorithms to perform standard modeling operations such as inverse evaluations, ray intersections, and surface-surface intersections on the GPU. Our modeling algorithms run in real time, enabling the user to sketch on the actual surface to create new features. In addition, the designer can edit the surface by interactively trimming it without the need for retessellation. Our GPU-accelerated algorithm to perform surface-surface intersection operations with NURBS surfaces can output intersection curves in the model space as well as in the parametric spaces of both the intersecting surfaces at interactive rates. We also extend our surface-surface intersection algorithm to evaluate self-intersections in NURBS surfaces.

A framework for biomechanics simulations using four-chamber cardiac models.

Computational cardiac models have been extensively used to study different cardiac biomechanics; specifically, finite-element analysis has been one of the tools used to study the internal stresses and strains in the cardiac wall during the cardiac cycle. Cubic-Hermite finite element meshes have been used for simulating cardiac biomechanics due to their convergence characteristics and their ability to capture smooth geometries compactly-fewer elements are needed to build the cardiac geometry-compared to linear tetrahedral meshes. Such meshes have previously been used only with simple ventricular geometries with non-physiological boundary conditions due to challenges associated with creating cubic-Hermite meshes of the complex heart geometry. However, it is critical to accurately capture the different geometric characteristics of the heart and apply physiologically equivalent boundary conditions to replicate the in vivo heart motion. In this work, we created a four-chamber cardiac model utilizing cubic-Hermite elements and simulated a full cardiac cycle by coupling the 3D finite element model with a lumped circulation model. The myocardial fiber-orientations were interpolated within the mesh using the Log-Euclidean method to overcome the singularity associated with interpolation of orthogonal matrices. Physiologically equivalent rigid body constraints were applied to the nodes along the valve plane and the accuracy of the resulting simulations were validated using open source clinical data. We then simulated a complete cardiac cycle of a healthy heart and a heart with acute myocardial infarction. We compared the pumping functionality of the heart for both cases by calculating the ventricular work. We observed a 20% reduction in acute work done by the heart immediately after myocardial infarction. The myocardial wall displacements obtained from the four-chamber model are comparable to actual patient data, without requiring complicated non-physiological boundary conditions usually required in truncated ventricular heart models.

Optimization Framework for Patient-Specific Cardiac Modeling.

PURPOSE: Patient-specific models of the heart can be used to improve the diagnosis of cardiac diseases, but practical application of these models can be impeded by the computational costs and numerical uncertainties of fitting mechanistic models to clinical measurements from individual patients. Reliable and efficient tuning of these models within clinically appropriate error bounds is a requirement for practical deployment in the time-constrained environment of the clinic. METHODS: We developed an optimization framework to tune parameters of patient-specific mechanistic models using routinely-acquired non-invasive patient data more efficiently than manual methods. We employ a hybrid particle swarm and pattern search optimization algorithm, but the framework can be readily adapted to use other optimization algorithms. RESULTS: We apply the proposed framework to tune full-cycle lumped parameter circulatory models using clinical data. We show that our framework can be easily adapted to optimize cross-species models by tuning the parameters of the same circulation model to four canine subjects. CONCLUSIONS: This work will facilitate the use of biomechanics and circulatory cardiac models in both clinical and research environments by ameliorating the tedious process of manually fitting the parameters.

A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves.

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.

Comparative Study of Community Acquired and Nosocomial Spontaneous Bacterial Peritonitis and its Variants in 150 Patients.

BACKGROUND: Nosocomial acquisition of spontaneous bacterial peritonitis (SBP) is debated as having a different microbial etiology and prognosis. Identification of clinical, laboratory predictors of mortality and appropriate empirical antimicrobial selection is necessary to prevent early mortality and morbidity. We aimed to find the clinical and bacteriological profile in nosocomial and community acquired SBP and its variants, and the predictors of mortality. MATERIAL AND METHODS: One hundred and fifty patients with 162 discrete episodes of different types of SBP were analyzed. Relevant clinical and laboratory data were analyzed. SBP was diagnosed according to standard criteria and classified as community acquired if the infection detected within 48 h of admission and as nosocomial after 48 h of admission to the hospital. RESULTS: Eighty seven percent had community acquired SBP (CSBP), 13% had nosocomial SBP (NSBP). Patients of NSBP were older, had more episodes of GI bleed and higher previous episodes of encephalopathy. Patients who died were older, had worse encephalopathy. NSBP had higher one month mortality. Age, serum sodium, encephalopathy and NSBP predicted mortality. Culture positivity was 22.22%. Escherichia coli was the commonest organism isolated. There was no difference in the bacteriological profile between CSBP and NSBP. E. coli showed up to 48% resistance to third generation cephalosporins. Overall sensitivity to aminoglycosides was more than 75%. CONCLUSIONS: Overall mortality was 59%. NSBP had significantly high one month mortality. Age, serum sodium, encephalopathy and NSBP were predictors of mortality. Bacteriological profile was similar between CSBP and NSBP.

Model of Human Fetal Growth in Hypoplastic Left Heart Syndrome: Reduced Ventricular Growth Due to Decreased Ventricular Filling and Altered Shape.

INTRODUCTION: Hypoplastic left heart syndrome (HLHS) is a congenital condition with an underdeveloped left ventricle (LV) that provides inadequate systemic blood flow postnatally. The development of HLHS is postulated to be due to altered biomechanical stimuli during gestation. Predicting LV size at birth using mid-gestation fetal echocardiography is a clinical challenge critical to prognostic counseling. HYPOTHESIS: We hypothesized that decreased ventricular filling in utero due to mitral stenosis may reduce LV growth in the fetal heart via mechanical growth signaling. METHODS: We developed a novel finite element model of the human fetal heart in which cardiac myocyte growth rates are a function of fiber and cross-fiber strains, which is affected by altered ventricular filling, to simulate alterations in LV growth and remodeling. Model results were tested with echocardiogram measurements from normal and HLHS fetal hearts. RESULTS: A strain-based fetal growth model with a normal 22-week ventricular filling (1.04 mL) was able to replicate published measurements of changes between mid-gestation to birth of mean LV end-diastolic volume (EDV) (1.1-8.3 mL) and dimensions (long-axis, 18-35 mm; short-axis, 9-18 mm) within 15% root mean squared deviation error. By decreasing volumetric load (-25%) at mid-gestation in the model, which emulates mitral stenosis in utero, a 65% reduction in LV EDV and a 46% reduction in LV wall volume were predicted at birth, similar to observations in HLHS patients. In retrospective blinded case studies for HLHS, using mid-gestation echocardiographic data, the model predicted a borderline and severe hypoplastic LV, consistent with the patients' late-gestation data in both cases. Notably, the model prediction was validated by testing for changes in LV shape in the model against clinical data for each HLHS case study. CONCLUSION: Reduced ventricular filling and altered shape may lead to reduced LV growth and a hypoplastic phenotype by reducing myocardial strains that serve as a myocyte growth stimulus. The human fetal growth model presented here may lead to a clinical tool that can help predict LV size and shape at birth based on mid-gestation LV echocardiographic measurements.