Computers in Biology and Medicine

In forensic odontology disaster victim identification, it is crucial to assess the similarity between post mortem (PM) dentitions and ante mortem (AM) dental records from a database. To facilitate ranking AM records by likelihood of a match, the similarity evaluation must yield an intuitive, quantitative score. This study introduces a scoring scheme designed to effectively distinguish 3D dentition surfaces acquired by intraoral 3D photo scans. The scoring scheme was validated on an independent dataset. The scoring scheme presented utilizes two levels of surface similarity, spanning from local similarity of surface representations to regional similarity based on relative keypoint placement. The scoring scheme demonstrated exceptional discriminatory power on the validation data, achieving a Receiver Operating Characteristic (ROC) area-under-the-curve (AUC) of 0.990 (95% CI 0.988 to 0.992). Implementing such a scoring system in disaster victim identification workflows, where AM 3D data can be procured, can provide an initial likelihood of matching, enabling forensic professionals to prioritize cases and allocate resources more efficiently based on objective measures of dental similarity.

BackgroundMultimodal large language models (MLLMs) have demonstrated substantial progress in medical and legal domains in recent years; however, their capabilities from the lens of forensic science--a field that is at the intersection of complex medical reasoning and legal interpretation, with conclusions critiqued by judicial scrutiny--remains largely unexplored. Forensic medicine uniquely depends on the accurate integration of often ambiguous text and visual information, yet systematic evaluations of MLLMs in this setting are lacking. MethodsWe conducted a comprehensive benchmarking study of eleven state-of-the-art MLLMs, including proprietary (GPT-4o, Claude 4 Sonnet, Gemini 2.5 Flash) and open-source (Llama 4, Qwen 2.5-VL) models. Models were evaluated on 847 examination-style forensic questions drawn from various academic literature, case studies, and clinical assessments, covering nine forensic subdomains. Both text-only and image-based questions were included. Model performance was assessed using direct and chain-of-thought prompting, with automated scoring verified through manual revision. ResultsPerformance improved consistently with newer model generations. Chain-of-thought prompting improved accuracy on text-based and choice-based tasks for most models, though this trend did not hold for image-based and open-ended questions. Visual reasoning and complex inference tasks revealed persistent limitations, with models underperforming in image interpretation and nuanced forensic scenarios. Model performance remained stable across forensic subdomains, suggesting topic type alone did not drive variability. ConclusionsMLLMs show emerging potential for forensic education and structured assessments, particularly for reinforcing factual knowledge. However, their limitations in visual reasoning, open-ended interpretation, and forensic judgment preclude independent application in live forensic practice. Future efforts should prioritize the development of multimodal forensic datasets, domain-targeted fine-tuning, and task-aware prompting to improve reliability and generalizability. These findings provide the first systematic baseline for MLLM performance in forensic science and inform pathways for their cautious integration into medico-legal workflows.

Rectal temperature measurement (RTM) from crime scenes is an important parameter for temperature-based time of death estimation (TDE). Various influential variables exist in TDE methods like the uncertainty in thermal and environmental parameters. Although RTM depends in particular on the location of measurement position, this relationship has never been investigated separately. The presented study fills this gap using Finite Element (FE) simulations of body cooling. A manually meshed coarse human FE model and an FE geometry model developed from the CT scan of a male corpse are used for TDE sensitivity analysis. The coarse model is considered with and without a support structure of moist soil. As there is no clear definition of ideal rectal temperature measurement location for TDE, possible variations in RTM location (RTML) are considered based on anatomy and forensic practice. The maximum variation of TDE caused by RTML changes is investigated via FE simulation. Moreover, the influence of ambient temperature, of FE model change and of the models positioning on a wet soil underground are also discussed. As a general outcome, we notice that maximum TDE deviations of up to ca. 2-3 h due to RTML deviations have to be expected. The direction of maximum influence of RTML change on TDE generally was on the line caudal to cranial.

In the context of dynamic image-based computational fluid dynamics (DIB-CFD) modeling of cardiac system, the role of sub-valvular apparatus (chordae tendineae and papillary muscles) and the effects of different mitral valve (MV) opening/closure dynamics, have not been systemically determined. To provide a partial filling of this gap, in this study we performed DIB-CFD numerical experiments in the left ventricle, left atrium and aortic root, with the aim of highlighting the influence on the numerical results of two specific modeling scenarios: i) the presence of the sub-valvular apparatus, consisting of chordae tendineae and papillary muscles; ii) different MV dynamics models accounting for different use of leaflet reconstruction from imaging. This is performed for one healthy and one MV regurgitant subjects. Specifically, a systolic wall motion is reconstructed from time-resolved Cine-MRI images and imposed as boundary condition for the CFD numerical simulation. Analyzing the numerical results, we found that sub-valvular apparatus do not affect the global fluid dynamics quantities, although it creates local variations, such as the developing of vortexes or flow disturbances, which lead to different stress distributions on cardiac structures. Moreover, different MV dynamics are considered starting from Cine-MRI MV segmentation at different temporal configurations, and then they are compared and managed numerically through a resistive approach. The obtained results highlight the importance of including a sophisticated diastolic model of MV dynamics, which accounts for MV geometries during diastasis and A-wave, in terms of describing the disturbed flow and ventricular turbulence. Statements and DeclarationsThe authors have no relevant financial or non-financial interests to disclose.

In this work we performed a computational image-based study of blood dynamics in the whole left heart, both in a healthy subject and in a patient with mitral valve regurgitation (MVR). We elaborated dynamic cine-MRI images with the aim of reconstructing the geometry and the corresponding motion of left ventricle, left atrium, mitral and aortic valves, and aortic root of the subjects. This allowed us to prescribe such motion to computational blood dynamics simulations where, for the first time, the whole left heart motion of the subject is considered, allowing us to obtain reliable subject-specific information. The final aim is to investigate and compare between the subjects the occurrence of turbulence and the risk of hemolysis and of thrombi formation. In particular, we modeled blood with the Navier-Stokes equations in the Arbitrary Lagrangian-Eulerian framework, with a Large Eddy Simulation model to describe the transition to turbulence and a resistive method to manage the valve dynamics, and we used a Finite Elements discretization implemented in an in-house code for the numerical solution. Our results highlighted that the regurgitant jet in the MVR case gave rise to a large amount of transition to turbulence in the left atrium resulting in a higher risk of formation of hemolysis. Moreover, MVR promoted a more complete washout of stagnant fiows in the left atrium during the systolic phase and in the left ventricle apex during diastole. NEW & NOTEWORTHYReconstruction from cine-MRI images of geometries and motion of the left heart (left atrium and ventricle, aortic root, aortic and mitral valve) of a healthy and mitral regurgitant patient. Prescription of such motion to a complete subject-specific computational fluid-dynamic simulation of the left heart. Investigation of turbulence in a regurgitant scenario. Study of the mechanisms of prevention from stagnant flows and hemolysis formation in the atrium.

BackgroundAortic annuloplasty, involving the implantation of an external ring around the aortic root to reduce annular dimensions, is a promising treatment for aortic valve insufficiency. However, its hemodynamic effects remain underexplored due to the absence of computational models validated by experimental and clinical data. MethodsThis study introduces a computational fluid-structure interaction (FSI) model of supra valvular aortic annuloplasty using 4D-flow magnetic resonance imaging (MRI). Native and post-annuloplasty conditions of idealized aortic root phantoms, including the aortic valve, were CAD-modelled and 3D-printed with elastic resin. These phantoms were tested in a mock circulatory flow-loop providing normal pulsatile physiologic conditions using a glycerol-water mixture to simulate blood viscosity. Flow and pressure data collected from sensors were used as boundary conditions for FSI simulations. Experimental velocity fields from 4D-flow MRI were compared to computational results to assess model accuracy. ResultsMRI scans of the annuloplasty model showed an increased peak systolic velocity (up to 145.4 cm/s) and localized flow alterations, corresponding to a higher pressure gradient across the valve. During regurgitation, the annuloplasty model showed broader velocity distributions compared to the native condition. The FSI simulations closely matched 4D-flow MRI data, with strong correlation coefficients (r > 0.93) and minimal Bland-Altman differences, particularly during systolic phases. ConclusionsThis study establishes an integrative methodology combining in-vitro, in-silico, and clinical imaging techniques to evaluate aortic annuloplasty hemodynamics. The validated digital twin framework offers a pathway for patient-specific modelling, enabling prediction of surgical outcomes and optimization of aortic valve repair strategies.

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

Analyzing patient data under current mechanical ventilation (MV) management processes is essential to understand MV consequences over time and to hypothesize improvements to care. However, progress is complicated by the complexity of lung-ventilator system (LVS) interactions, patient-care and patient-ventilator heterogeneity, and a lack of classification schemes for observable behavior. Ventilator waveform data originate from patient-ventilator interactions within the LVS while care processes manage both patients and ventilator settings. This study develops a computational pipeline to segment joint waveform and care settings timeseries data into phenotypes of the data generating process. The modular framework supports many methodological choices for representing waveform data and unsupervised clustering. The pipeline is generalizable although empirical output is data- and algorithm-dependent. Applied individually to 35 ARDS patients including 8 with COVID-19, a median of 8 phenotypes capture 97% of data using naive similarity assumptions on waveform and MV settings data. Individuals phenotypes organize around ventilator mode, PEEP, and tidal volume with additional delineation of waveform behaviors. However, dynamics are not solely driven by setting changes. Fewer than 10% of phenotype changes link to ventilator settings directly. Evaluation of phenotype heterogeneity reveals LVS dynamics that cannot be discretized into sub-phenotypes without additional data or alternate assumptions. Individual phenotypes may also be aggregated for use in scalable analysis, as behaviors in the 35 patient cohort comprise 16 cohort-scale LVS types. Further, output phenotypes compactly discretize the data for longitudinal analysis and may be optimized to resolve features of interest for specific applications.

ObjectiveNowadays, the treatment of mitral valve prolapse involves two distinct repair techniques: chordal replacement (Neochordae technique) and leaflet resection (Resection technique). However, there is still a debate in the literature about which is the optimal one. In this context, we performed an image-based computational fluid dynamic study to evaluate blood dynamics in the two surgical techniques. MethodsWe considered a healthy subject (H) and two patients (N and R) who underwent surgery for the prolapse of the posterior leaflet and were operated with the Neochordae and Resection technique, respectively. Computational Fluid Dynamics (CFD) was employed with prescribed motion of the entire left heart coming from cine-MRI images, with a Large Eddy Simulation model to describe the transition to turbulence and a resistive method for managing valve dynamics. We created three different virtual scenarios where the operated mitral valves were inserted in the same left heart geometry of the healthy subject to study the differences attributed only to the two techniques. ResultsWe compared the three scenarios by quantitatively analyzing ventricular velocity patterns and pressures, transition to turbulence, and the ventricle ability to prevent thrombi formation. From these results we found that both the operated cases were able to restore almost physiological blood dynamic conditions, with some differences due to the reduced mobility of the Resection posterior leaflet. Conclusions: Our findings suggest that the Neochordae technique developed a slightly more physiological flow with respect to the Resection technique. The latter gave rise to a different direction of the mitral jet during diastole increasing the turbulence that is associated with ventricular effort and hemolysis, with also a larger ability to washout the ventricular apex preventing from thrombi formation.

BackgroundPatient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. MethodsWe developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. ResultsThe LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). ConclusionsOverall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registrationApproval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

Cardiovascular and respiratory diseases (CVRD) are the leading causes of death worldwide. The construction of health digital twins for patient monitoring is becoming a fundamental tool to reduce invasive procedures, lower healthcare costs, minimize patient hospitalization, design clinical trials and personalize therapies. The aim of this study is to investigate the feasibility of machine learning-based monitoring of healthy subjects and CVRD patients in an in silico context. In particular, a population of virtual subjects, both healthy and with CVRD, was created using a comprehensive zero-dimensional global closed-loop model. Then, we trained Gaussian process regression (GPR) models, informed by wearable-acquired data, to predict variables normally acquired with invasive or operator-dependent methods. Presented results demonstrate, in an in silico setting, the feasibility of GRP-based prediction of in-hospital variables from wearable-derived indexes. Author summaryCardiovascular and respiratory diseases are major global health concerns. Effective remote monitoring is essential for early detection of complications and improved patient care, especially for people with chronic conditions. Wearable devices provide a non-invasive way to track health indicators, but they do not directly measure certain key physiological parameters that doctors typically assess in hospitals. In this study, we explore how machine learning approaches can help bridge this gap. By using virtually-generated data, we trained Gaussian process regression models to estimate critical cardiovascular and respiratory indexes, such as cardiac output and oxygen levels. In particular, we created a virtual population of simulated patients and used their data to train and test our model. Our findings suggest that this approach can be a valuable tool for remote monitoring, providing healthcare professionals with accurate insights, without the need for invasive procedures and enabling earlier detection of complications. However, further testing with real patient data is necessary to fully assess its clinical potential.

Over the past few decades, osmotherapy has commonly been employed to reduce intracranial pressure in post-stroke oedema. However, evaluating the effectiveness of osmotherapy has been challenging due to the difficulties in clinical intracranial pressure measurement. As a result, there are no established guidelines regarding the selection of administration protocol parameters. Considering that the infusion of osmotic agents can also give rise to various side effects, the effectiveness of osmotherapy has remained a subject of debate. In previous studies, we proposed the first mathematical model for the investigation of osmotherapy and validated the model with clinical intracranial pressure data. The physiological parameters vary among patients and such variations can result in the failure of osmotherapy. Here, we propose an AI-assisted in-silico trial for further investigation of the optimisation of administration protocols. The proposed deep neural network predicts intracranial pressure evolution over osmotherapy episodes. The effects of the parameters and the choice of dose of osmotic agents are investigated using the model. In addition, clinical stratifications of patients are related to a brain model for the first time for the optimisation of treatment of different patient groups. This provides an alternative approach to tackle clinical challenges with in-silico trials supported by both mathematical/physical laws and patient-specific biomedical information.

Heart disease is the leading cause of death worldwide, contributing to millions of fatalities each year. Early detection and accurate risk prediction are therefore critical for timely intervention and improved patient outcomes. In this study, seven different Machine Learning (ML) prediction models were tested on the collections of heart disease datasets. The data was divided, with 80% used to train the models and 20% saved to test them later. The settings for the models were carefully adjusted using a method that checks their performance multiple times to find the best ones. The models were then evaluated on the unseen test data. Their performance was measured using several metrics (Accuracy, Recall, Precision, PR-AUC and ROC-AUC), and their results were further examined using a confusion matrix. In addition to traditional evaluation metrics, SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contribution of each feature to the models predictions. It was observed that the Multi-Layer Perceptron (MLP) achieved the highest performance on both datasets, demonstrating strong predictive capability while remaining interpretable through the integration of SHAP. This study shows that modern ML models can be very good at predicting heart disease risk, and provide explainable performance. This study provides an effective approach for predicting heart disease risk with an explainable model to help doctors choose the best tool.

BackgroundPreeclampsia (PE) is a severe hypertensive disorder of pregnancy driven by placental dysfunction. A complex interplay of inflammatory, metabolic, and endothelial pathways regulates the transition from a healthy pregnancy to a pathological state. However, the systems-level dynamics and the mechanisms that the predisposing factors determine diseases onset and progression remain to be uncovered. MethodologyAn integrative systems biology approach was used to develop a representative PE time-aware network, using a temporal transcriptomic dataset from uncomplicated gestation. By mapping a curated PPI of PE onto normal placental DEGs, a dynamic, 30-node Boolean network model was constructed with rules curated from established signaling pathways. The main outcome that was used to represent a PE system state was the inactivation of eNOS. The model was used to identify stable cellular states (attractors) and to simulate the response to systemic conditions known to be involved in pathology, including inflammation, hypoxia, oxidative stress and metabolic overload (oxLDL). Complementary network diffusion analysis was performed expanding the PE-time-aware direct network to identify key propagating pathways from signals evoked in different gestational periods. ResultsThe model revealed that the networks intrinsic dynamics converge towards an endothelial preserve function. Additionally, using the DEGs of normal gestation as initial state has shown to preserve eNOS activity. Pathological perturbations, however, drove the system into a stable attractor characterized by the loss of eNOS activity. Crucially, two etiologically distinct pathways to the vascular dysfunction were identified: i. an inflammatory NF-kB/TNF mediated which was triggered by either oxLDL or Inflammation inputs; and ii.a "direct vascular-driven" pathway by oxidative stress. Despite their different routes, both pathways were characterized by inactive AKT1. Perturbation experiments confirmed that AKT1 was the critical and main regulator of endothelial NOS function. While blocking upstream inflammation nodes did not prevent eNOS loss, the constitutive activation of AKT1 was consistent to maintain eNOS activity, superimposing any other pathological negative outcomes on vascular function. ConclusionThis work raises AKT1 as a pivotal node to maintain endothelial NOS activity, even in the presence of oxidative and inflammation conditions. The model developed provides a mechanistic basis for the clinical heterogeneity of PE. At least two distinct molecular pathways were identified as "routes" that primed the network to PEs attractors, i.e.: inactivation of eNOS. Nevertheless, AKT1 failure or inactive state was a sine quo non condition to drive PE. The findings imply that therapeutic approaches, including metformin, which aim to support or improve AKT1 and its downstream signaling, may be effective in promoting vascular health in pregnancies at high risk.

A cardiac condition known as hypertrophic cardiomyopathy (HCM) is characterized by an irregular thickening of the heart muscle. There is still much to learn about its molecular mechanics. In order to pinpoint important genes and regulatory abnormalities in HCM, this work offers a thorough computational analysis of gene expression data. Two strategies are employed here. Initially, hub genes were identified, co-expression networks were constructed, gene modules were detected, and they were linked to clinical characteristics using Weighted Gene Co-expression Network Analysis (WGCNA). Second, the same dataset was subjected to three different gene selection techniques: variance-based filtering, volcano plot analysis, and a Genetic Algorithm for Novel Gene Acquisition (GANGA). For the first time, GANGA successfully incorporates a previously defined objective function from simulated annealing into a genetic algorithm. Additionally, it uses two-point crossover, meticulous parameter optimization, and customizable elitism. Three genes were shown to be shared by all approaches, including WGCNA: RASID1, CEBPD, and S100A9. Through enrichment analysis, these were confirmed to be implicated in pathways linked to inflammation. Their incorporation into cytokine-driven networks was validated by investigation of protein-protein interactions. S100A9 emerged as a crucial regulator that activates RASD1 in illness, according to co-expression networks constructed for normal and HCM samples, which showed changed regulatory patterns. The methodological development of modifying and optimizing a simulated annealing-based objective function within a GA framework in GANGA for efficient gene selection, as well as the comprehensive multi-method pipeline for HCM analysis, are what make this study distinctive.

BackgroundAlthough the benefits of machine learning (ML) are undeniable in health-care, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This work proposes discernibility as a new approach to faithfully reflect human cognition, with the users perception of a relationship between explanations and data for a given variable. MethodsA total of 50 participants (19 biomedical and 31 data scientists) evaluated their perception of the discernibility of explanations from both synthetic and human-based dataset (National Health and Nutrition Examination Survey). The inter-rater reliability was tested through the intraclass correlation coefficient (ICC). 13 statistical coefficients were considered to be able to capture for a given variable the relationship between its values and its explanations. A Passing-Bablok regression was performed for each user to highlight the consistency between user rating and each coefficient. FindingsThe low inter-rater reliability of discernibility (ICC{inverted exclamation}0.5) with no difference between areas of expertise or level of education underlines the need for an objective metric of discernibility. Among all evaluated metrics, dcor metric was found to be the most suitable to capture the intra-individual reliability of discernibility perceived by users (median slope closer to 1 and a narrower confidence interval width for the Passing-Bablok regression with the lowest differential bias between the most and least discernible values). Interpretationdcor was shown to be a reliable metric for assessing the discernibility of explanations, effectively capturing the clarity of the relationship between the data and their explanations, and providing clues to the underlying pathophysiological mechanisms that are not immediately apparent when examining individual predictors. Discernibility can also serve as an evaluation metric for model quality, used to prevent overfitting or aid in feature selection, providing medical practitioners with more accurate and persuasive results.

A recent study by Kawatou et al. has shown that the local heterogeneity of ion channel conductance is a critical substrate for focal or reentrant arrhythmias. However, the role of fibroblasts with repolarization heterogeneity in the initiation and maintenance of arrhythmias remains unknown. In this study, we investigated how diffuse fibrosis contributes to the formation of focal and reentrant arrhythmias under drug-induced heterogeneity using physiologically detailed mathematical models of the human heart. To simulate drug-induced heterogeneity, we varied the maximum conductance of transmembrane potassium and calcium currents, leading to heterogeneity in action potential duration (APD). Then, we assessed the effects of different fibrosis densities (FD) on the occurrence of premature ventricular complexes (PVCs). Fibroblasts were randomly and evenly inserted into the tissue, and various FD levels ranging from 0 to 35% were examined. We found a biphasic relationship between FD and drug-induced PVCs. Within a certain range of FD, FD positively correlated with PVC susceptibility. However, excessively high fibrosis levels were associated with reduced susceptibility to PVCs. In addition, the self-sustainability of arrhythmias exhibited a positive correlation with FD. This study demonstrates the interplay between the diffuse fibrosis and the drug-induced heterogeneity of APD in the genesis of ventricular arrhythmias. Author summarySudden cardiac death remains a leading cause of death worldwide. Understanding the mechanisms underlying arrhythmia and its precursors is critical for the development of effective therapies and drugs. Repolarization heterogeneity plays a crucial role in both the initiation and maintenance of arrhythmias. Fibroblasts constitute a vital component of cardiac structure, originating from the remodeling of ventricular wall cells or the transformation of injured myocardial cells. Fibroblasts are known to couple with and alter the electrical properties of myocardial cells. However, our understanding of the role of fibroblasts in the development of arrhythmia remains limited. In this study, we employed a physiologically detailed mathematical model of cardiac tissue to investigate the roles of drug-induced heterogeneity and diffuse fibrosis in the initiation and maintenance of arrhythmias. We used 2D and 3D computational models to simulate various levels of drug-induced heterogeneity conditions with normal to pathological levels of fibroblast density (FD). We found that within a certain range of FD, fibroblasts promote PVCs under drug-induced heterogeneity. However, if FD exceeds 30%, the occurrence of PVCs decreases (biphasic relationship). On the other hand, the self-sustainability of VF (ventricular fibrillation) consistently increases with FD. This study implies that fibroblasts in cardiac tissue may play different roles in the initiation and maintenance of arrhythmia.

Intracardiac hemodynamics plays a crucial role in the onset and development of cardiac and valvular diseases. Simulations of blood flow in the left ventricle (LV) have provided valuable insight into assessing LV hemodynamics. While fully coupled fluid-solid modelings of the LV remain challenging due to the complex passive-active behavior of the LV wall myocardium, the integration of imaging-driven quantification of structural motion with computational fluid dynamics (CFD) modeling in the LV holds the promise of feasible and clinically translatable characterization of patient-specific LV hemodynamics. In this study, we propose to integrate two magnetic resonance imaging (MRI) modalities with the moving-boundary CFD method to characterize intracardiac LV hemodynamics. Our method uses the standard cine cardiac magnetic resonance (CMR) images to estimate four-dimensional myocardial motion, eliminating the need for involved myocardial material modeling to capture LV wall behavior. In conjunction with CMR, phase contrast-MRI (PC-MRI) was used to measure temporal blood inflow rates at the mitral orifice, serving as an additional boundary condition. Flow patterns, including velocity streamlines, vortex rings, and kinetic energy, were characterized and compared to the available data. Moreover, relationships between LV wall kinematic markers and flow characteristics were determined without myocardial material modeling and using a non-rigid image registration (NRIR) method. The fidelity of the simulation was quantitatively evaluated by validating the flow rate at the aortic outflow tract against respective PC-MRI measures. The proposed methodology offers a novel and feasible toolset that works with standard PC-CMR protocols to improve the clinical assessment of LV characteristics in prognostic studies and surgical planning.

BackgroundExtracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients. MethodsWe analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. ResultsPOD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. ConclusionsModal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

PurposePulmonary valve replacement (PVR) using bioprosthetic valves is a common procedure performed in patients with repaired Tetralogy of Fallot and other conditions, but these valves frequently become dysfunctional within 15 years of implantation. Since PVR is often performed in adolescence, valves are typically oversized to account for somatic growth. However, the contribution of oversizing to early valve failure are not clearly understood. The purpose of this study was to explore the impact of valve sizing and orientation on local hemodynamics and valve performance. MethodsDifferent valve sizes were represented by changing the cardiac output through a 25 mm bioprosthetic valve implanted in an idealized 3D-printed model of the right ventricular outflow tract (RVOT). The local hemodynamics at three valve sizes and two valve orientations were assessed using 4D-Flow MRI and high-speed camera imaging. ResultsNoticeable differences in jet asymmetry, the amount of recirculation, leaflet opening patterns, as well as the size and location of reversed flow regions were observed with varying valve sizes. Rotation of the valve resulted in drastic differences in reversed flow regions, but not forward flow. ConclusionFlow features observed in the oversized valve in this study have previously been correlated with calcification, hemolysis, and leaflet fatigue. Therefore, valve oversizing can negatively impact local hemodynamics and leaflet performance.