ArticleNPJ digital medicine2024
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technologies, Clinical Applications, and Implementation Barriers of Digital Twins in Precision Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- A physics-informed eikonal model for simulating arrhythmias in the human heart in real-time.Nature communications · 2026Article
- Cardiac remodelling in type 2 diabetes: Pathophysiological mechanisms and opportunities for multiscale computational modelling and simulation.The Journal of physiology · 2026Review
- Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns.The Journal of physiology · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- Feel the force: Biomechanical homeostasis of the cardiovascular system.Journal of translational internal medicine · 2026Article
- Mapping the future of medicine through digital twins.Frontiers in molecular medicine · 2026Review
- Article
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Article
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Synthetic generation of cardiac tissue motion from surface electrocardiograms.Nature cardiovascular research · 2025Article
- Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.NPJ digital medicine · 2025Review
- The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective.PloS one · 2025Article
- Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators.NPJ digital medicine · 2024Article
- Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways.Frontiers in physics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.