Branched Latent Neural Maps

Salvador, Matteo; Marsden, Alison Lesley

doi:10.1016/j.cma.2023.116499

Cited by 4 publications

(13 citation statements)

References 48 publications

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“…We automatically extract the surface meshes from the segmentations using the marching cube algorithm [31] and truncate the base myocardium above a manually identified plane to create a biventricular surface mesh. We subsequently use TetGen [55] to create the tetrahedral volumetric mesh with a maximum edge size of 1 mm [53,62]. The torso model is created semi-automatically from the images using thresholdand region-growing-based segmentation methods.…”

Section: Pre-processingmentioning

confidence: 99%

“…We construct a geometry-specific surrogate model of cardiac function by building a feedforward partially-connected NN that explores the variability of our physics-based electrophysiology model detailed in Section 3.2 while structurally separating the role of temporal t and functional θ EP parameters. This recent scientific machine learning tool, proposed in [53], allows for different levels of disentanglement between inputs and outputs. The surrogate model reads:…”

Section: Branched Latent Neural Mapsmentioning

confidence: 99%

“…Indeed, we reconstruct I(t), II(t), III(t), aV L(t), aV R(t) and aV F (t) a posteriori by means of Equations ( 3) and (4). Furthermore, vector z(t) leverages some z latent (t) latent variables that enhance the learned temporal dynamics by acting in regions with steep gradients [53].…”

Section: Branched Latent Neural Mapsmentioning

confidence: 99%

“…Time t ∈ [0, 1] and model parameters θ EP ∈ [−1, 1] N P are also normalized during the training and testing phases. We refer to [53] for a detailed description of all the properties related to BLNMs that enable them to effectively learn complex physical processes.…”

Section: Branched Latent Neural Mapsmentioning

confidence: 99%

“…• A recently proposed scientific machine learning method, namely Branched Latent Neural Maps (BLNMs) [53], to build an accurate and efficient dynamic surrogate model of cardiac function…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Digital twinning of cardiac electrophysiology for congenital heart disease

Salvador,

Kong,

Peirlinck

et al. 2023

Preprint

Self Cite

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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.

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Section: Pre-processingmentioning

confidence: 99%

Section: Branched Latent Neural Mapsmentioning

confidence: 99%

Section: Branched Latent Neural Mapsmentioning

confidence: 99%

Section: Branched Latent Neural Mapsmentioning

confidence: 99%

“…• A recently proposed scientific machine learning method, namely Branched Latent Neural Maps (BLNMs) [53], to build an accurate and efficient dynamic surrogate model of cardiac function…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Digital twinning of cardiac electrophysiology for congenital heart disease

Salvador,

Kong,

Peirlinck

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

An optimization framework to personalize passive cardiac mechanics

Shi,

Chen,

Takayama

et al. 2024

Computer Methods in Applied Mechanics and Engineering

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Digital twinning of cardiac electrophysiology for congenital heart disease

Salvador,

Kong,

Peirlinck

et al. 2024

J. R. Soc. Interface.

View full text Add to dashboard Cite

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.

show abstract

Branched Latent Neural Maps

Cited by 4 publications

References 48 publications

Digital twinning of cardiac electrophysiology for congenital heart disease

Digital twinning of cardiac electrophysiology for congenital heart disease

An optimization framework to personalize passive cardiac mechanics

Digital twinning of cardiac electrophysiology for congenital heart disease

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