arXiv subjects

Maxx Holmes

Publications and source records attributed to Maxx Holmes.

3 recordsLinked to original sources

arXiv2025

Female anatomies disguise ECG abnormalities following myocardial infarction: an AI-enabled modelling and simulation study

The electrocardiogram (ECG) is modulated by torso-heart anatomy, and this challenges patients' diagnosis and risk stratification. This study aims to quantify how torso-heart anatomical factors affect sex-differences in ECG biomarkers in acute and chronic myocardial infarction (MI). We exploit the perfect control of AI-augmented multiscale modelling and simulation, based on clinical magnetic resonance imaging (MRI) data and ECGs for model construction and validation. A cohort of 1720 torso-ventricular anatomies (50% female) was constructed from MRIs of healthy and post-MI participants in the UK Biobank study. 8600 ECG simulations were performed considering anatomical variability and 3 electrophysiological stages (healthy, acutely ischemic, and infarcted). The effect of cardiac size, position, and orientation on each ECG biomarker was quantified. Female anatomies had larger distances between the infarct and ECG electrodes (relative to cardiac size), and larger angles between the infarct normal and ECG lead axes, both primarily caused by their more superior cardiac position. This reduced ST-elevation and caused low-amplitude late depolarisation and repolarisation tails to be missed, shortening QRS duration (QRSd) and T-peak-to-end interval (TpTe). The position and orientation of the heart impacted TpTe more severely than QRSd. AI-enabled mechanistic modelling and simulation identify smaller ventricles, superior cardiac position, and different ventricular orientation as key anatomical contributors of shorter QRS and T waves, and lower ST-elevation, in female versus male anatomies. This provides a blueprint for quantifying the impact of anatomical sex differences on functional markers and enables future work in tailoring clinical guidelines considering unique patient anatomy to reduce biased outcomes.

physics.med-ph↗

arXiv2025

An Automated Computational Pipeline for Generating Large-Scale Cohorts of Patient-Specific Ventricular Models in Electromechanical In Silico Trials

In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In silico trials not only minimise patient risks but also reduce reliance on animal testing. However, the implementation of in silico trials presents several time-consuming challenges. It requires the creation of large cohorts of virtual patients. Each virtual patient is described by their anatomy with a volumetric mesh and electrophysiological and mechanical dynamics through mathematical equations and parameters. Furthermore, simulated conditions need definition including stimulation protocols and therapy evaluation. For large virtual cohorts, this requires automatic and efficient pipelines for generation of corresponding files. In this work, we present a computational pipeline to automatically create large virtual patient cohort files to conduct large-scale in silico trials through cardiac electromechanical simulations. The pipeline generates the files describing meshes, labels, and data required for the simulations directly from unprocessed surface meshes. We applied the pipeline to generate over 100 virtual patients from various datasets and performed simulations to demonstrate capacity to conduct in silico trials for virtual patients using verified and validated electrophysiology and electromechanics models for the context of use. The proposed pipeline is adaptable to accommodate different types of ventricular geometries and mesh processing tools, ensuring its versatility in handling diverse clinical datasets. By establishing an automated framework for large scale simulation studies as required for in silico trials and providing open-source code, our work aims to support scalable, personalised cardiac simulations in research and clinical applications.

cs.CE↗

arXiv2024

Cardiac Digital Twin Pipeline for Virtual Therapy Evaluation

Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an automated pipeline for personalising ventricular anatomy and electrophysiological function based on routinely acquired cardiac magnetic resonance (CMR) imaging data and the standard 12-lead electrocardiogram (ECG). Using CMR-based anatomical models, a sequential Monte-Carlo approximate Bayesian computational inference method is extended to infer electrical activation and repolarisation characteristics from the ECG. Fast simulations are conducted with a reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics for repolarisation. For each patient, parameter uncertainty is represented by inferring a population of ventricular models rather than a single one, which means that parameter uncertainty can be propagated to therapy evaluation. Furthermore, we have developed techniques for translating from reaction-Eikonal to monodomain simulations, which allows more realistic simulations of cardiac electrophysiology. The pipeline is demonstrated in a healthy female subject, where our inferred reaction-Eikonal models reproduced the patient's ECG with a Pearson's correlation coefficient of 0.93, and the translated monodomain simulations have a correlation coefficient of 0.89. We then apply the effect of Dofetilide to the monodomain population of models for this subject and show dose-dependent QT and T-peak to T-end prolongations that are in keeping with large population drug response data.

cs.CE↗