Cardiovascular Digital Twins from Physics Based to Data Driven Approaches

• URL: http://arxiv.org/abs/2608.02135v1
• Date: Mon, 03 Aug 2026 12:23:44 GMT
• Title: Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
• Abstract summary: Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation.<n>Mechanistic models provide physiological interpretability but remain computationally demanding, whereas data-driven approaches improve scalability yet risk limited robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation. Mechanistic models provide physiological interpretability but remain computationally demanding, whereas data-driven approaches improve scalability yet risk limited robustness. Emerging physics-informed, graph-based, and hybrid methods integrate physical constraints with relational learning across vascular networks. We review modelling paradigms, data assimilation frameworks, validation challenges, and translational pathways toward clinically deployable cardiovascular digital twins.

Related papers

• Multimodal Graph-based Classification of Esophageal Motility Disorders [73.90451172929117]
  Diagnosing esophageal motility disorders pose significant challenges due to the complexity of high-resolution impedance manometry data and variability in clinical interpretation.<n>This work explores the feasibility of a multimodal Machine Learning-based classification approach that combines HRIM recordings with patient-specific information and incorporates a graph-based modeling of esophageal physiology.
  arXiv  Detail & Related papers  (2026-05-13T14:52:12Z)
• From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems [51.82266520875928]
  This survey introduces physically constrained executability as a unifying perspective for comparing approaches at the level of execution.<n>I propose a taxonomy of execution regimes ranging from isolated offline models to coordinated co-simulation, to continuously executing digital twins sustained by online data.<n>The executability concept clarifies why accuracy alone is insufficient, and motivates an agenda centered on semantic interoperability, hybrid-time correctness, evaluation protocols, scalable assimilation, and safe closed-loop validation.
  arXiv  Detail & Related papers  (2026-04-15T07:34:46Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins [33.58746991219774]
  PINS-CAD is a physics-informed self-supervised learning framework.<n>It pre-trains graph neural networks on 200,000 synthetic coronary digital twins to predict pressure and flow.<n> PINS-CAD predicts future cardiovascular events with an AUC of 0.73, outperforming clinical risk scores and data-driven baselines.
  arXiv  Detail & Related papers  (2025-11-25T15:01:46Z)
• A Brief History of Digital Twin Technology [10.744216642614214]
  Digital twin technology has advanced through industrial adoption to spark a healthcare transformation.<n>A digital twin is a data-driven virtual counterpart of a physical system, continuously updated through real-time data streams.<n>In medicine, digital twin integrates imaging, biosensors, and computational models to generate patient-specific simulations.
  arXiv  Detail & Related papers  (2025-11-24T02:03:55Z)
• Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning [15.106435744696013]
  A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features. We propose a method to identify digital twin model parameters using only noninvasive patient health data.
  arXiv  Detail & Related papers  (2024-02-29T23:04:42Z)
• Digital Twins for Patient Care via Knowledge Graphs and Closed-Form Continuous-Time Liquid Neural Networks [0.0]
  Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. The challenge of modeling complex diseases with multimodal patient data and the computational complexities of analyzing it have stifled digital twin adoption in the biomedical vertical. This paper proposes a novel framework for addressing the barriers to clinical twin modeling created by computational costs and modeling complexities.
  arXiv  Detail & Related papers  (2023-07-08T12:52:31Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Graph representation forecasting of patient's medical conditions: towards a digital twin [0.0]
  We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions. We provide a proof of concept of integrating a large set of composable clinical models using molecular data.
  arXiv  Detail & Related papers  (2020-09-17T13:49:48Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.