From stethoscopes to supercomputers: How digital twins and AI are transforming cardiovascular medicine
For centuries, doctors have assessed heart health through sound and rhythm—the familiar lub-dub heard through a stethoscope representing the limits of cardiac diagnosis. Today, a revolutionary approach is transforming cardiology: computational physiology. This emerging field uses sophisticated computer modeling to simulate the intricate workings of the human heart, offering unprecedented insights into cardiac health and disease.
With cardiovascular diseases claiming approximately 17 million lives annually worldwide 1 , the stakes for innovation have never been higher.
Annual deaths from cardiovascular diseases
The Virtual Physiological Human (VPH) initiative and Physiome Project stand at the forefront of this revolution, working to create comprehensive computer models that can predict how an individual's heart will behave under various conditions 4 8 . These digital replicas of human hearts are not just research tools—they're paving the way for truly personalized cardiac care that can account for everything from genetic variants to specific cellular processes.
The Virtual Physiological Human (VPH) represents a paradigm shift in how we approach heart health. This worldwide initiative aims to develop "next-generation computer technologies to integrate all information available for each patient" and generate predictive models of their health outcomes 8 .
Rather than relying solely on statistical averages from population studies, the VPH initiative seeks to create models constrained by the laws of physics and containing a "comprehensive knowledge graph of all human physiology and anatomy" 4 .
The VPH is part of the broader IUPS Physiome Project, which aims to build virtual models that span from organ systems down to protein function, creating a multiscale understanding of human physiology 4 .
These technologies are designed to "integrate all information available for each patient" to predict health outcomes under various conditions.
The ultimate expression of this approach is the development of digital twins—virtual replicas of individual patients' hearts that can be tested, probed, and analyzed without risk. These models "represent virtual replicas that encapsulate both medical and physiological characteristics of patients" and "facilitate a deeper understanding of disease progression" while optimizing treatment plans 4 .
One remarkable application of computational physiology comes from recent research presented at the American College of Cardiology's 2025 Scientific Session. Researchers developed a deep learning model trained to detect blocked coronary arteries from electrocardiogram (ECG) readings—a common but challenging task in emergency medicine 2 .
The research team faced a significant challenge: while ECGs can quickly detect some types of heart attacks, the signatures of non-ST-segment elevation myocardial infarction (NSTEMI) are often subtle and difficult to interpret. This frequently leads to dangerous treatment delays for patients who urgently need coronary revascularization 2 .
The model was trained on health records from nearly 145,000 emergency department visits at a single U.S. medical center.
The model was tested on 35,000 visits from the same source, then externally validated using records from 18,000 emergency department visits at a German medical center.
The AI classified each patient as low, intermediate, or high risk based solely on ECG readings.
Researchers compared the model's accuracy against clinician interpretation, conventional troponin testing, and high-sensitivity troponin T testing 2 .
The performance of this AI model was striking. It achieved an area under the curve (AUC) of 0.91 in the internal test cohort, significantly outperforming both clinician ECG interpretation (0.65) and conventional troponin testing (0.71) 2 . In medical statistics, AUC measures how well a model can distinguish between classes, with 1.0 representing perfect discrimination.
| Diagnostic Method | Internal Test Cohort (AUC) | External Validation Cohort (AUC) |
|---|---|---|
| AI ECG Model | 0.91 | 0.85 |
| Clinician ECG Interpretation | 0.65 | 0.74 |
| Conventional Troponin Testing | 0.71 | - |
| High-Sensitivity Troponin T | - | 0.87 |
Perhaps most importantly, the researchers discovered cases where heart attacks had occurred but were initially missed—situations where the model would have provided correct identification hours earlier 2 . This time savings could be crucial for preserving heart muscle and improving patient outcomes.
AI detection of heart attacks compared to standard methods
The model also featured self-explainability, allowing researchers to identify the features the model used for decision-making and connect them with clinically established markers 2 . This transparency is vital for building trust among medical professionals who need to understand how AI reaches its conclusions.
Computational physiology employs a diverse array of modeling techniques, each with specific strengths for different research questions. A 2024 study in Scientific Reports evaluated five mathematical approaches to simulate cardiac electrophysiology 9 .
The most detailed model is the EMI model (Extracellular, Membrane, Intracellular), which distinctly separates these three domains and represents individual cells. This model provides sub-cellular resolution, making it ideal for studying processes at the micrometer level but requiring substantial computational resources 9 .
For larger tissue simulations, researchers often use the bidomain (BD) and monodomain (MD) models. These are based on homogenization approaches that average electrical properties across many cells, making them suitable for simulating larger tissue masses but lacking detailed cellular resolution 9 .
Intermediate approaches include the Kirchhoff network model (KNM) and simplified Kirchhoff network model (SKNM), which represent individual cells but with less computational demand than the EMI model 9 .
| Model Type | Resolution | Best Application | Computational Demand |
|---|---|---|---|
| EMI Model | Sub-cellular | Micrometer-level processes | Very High |
| Bidomain (BD) | Tissue level | Large tissue masses | Moderate-High |
| Monodomain (MD) | Tissue level | Large tissue masses | Moderate |
| Kirchhoff Network (KNM) | Cellular | Small tissue samples | Low-Moderate |
| Simplified KNM | Cellular | Small tissue samples | Low |
In computational physiology, "reagents" take the form of datasets, algorithms, and software tools:
Studies like the VA Million Veteran Program, which analyzed genetic data from over 95,000 participants of African ancestry, enable discovery of population-specific risk factors 6 .
The PhysioNet CinC 2016 Challenge dataset containing 3,240 heart sound samples supports development of automated diagnostic tools 5 .
Convolutional Neural Networks (CNNs) with attention mechanisms like the Convolutional Block Attention Module (CBAM) enable automated feature extraction from medical signals 5 .
Packages like Alya, designed for high-performance computing systems, implement complex models such as porohyperelastic frameworks for simulating biological tissues 4 .
Computational physiology becomes even more powerful when integrated with genomics. Recent research identified a common variant in the CD36 gene present in 17% of people with African ancestry that substantially increases the risk of dilated cardiomyopathy 6 .
This variant acts differently than most known genetic causes of heart disease. Rather than directly damaging the heart's contractile machinery, it disrupts how the heart takes up and converts certain fuels into energy. Over time, this energy shortfall weakens the heart muscle, ultimately leading to heart failure 6 .
| Variant Status | Prevalence in African Ancestry | Increased Risk of Dilated Cardiomyopathy |
|---|---|---|
| One copy | 1 in 6 people | 33% higher risk |
| Two copies | 1 in 100 people | 3-fold higher risk |
This discovery demonstrates how computational analysis of diverse genomic datasets can uncover previously unknown disease mechanisms. The CD36 variant alone was estimated to account for roughly one-fifth of the observed difference in dilated cardiomyopathy risk between people of African and European ancestry 6 .
The convergence of computational modeling, artificial intelligence, and clinical medicine points toward a future where digital heart twins will become routine in cardiovascular care. This approach involves constructing "patient-specific virtual hearts using mechanistic and statistical models informed by disease-relevant variables" .
When signs of heart disease are detected, sex-specific digital twins can be generated and exposed to thousands of drug simulations. The treatment demonstrating optimal efficacy on the patient's digital twin can then be selected for clinical use . This represents the ultimate convergence of computational modeling and personalized medicine.
Drug testing on digital twins before clinical use
"The integration of artificial intelligence and machine learning techniques, which have seen rapid growth and adoption in recent years and months, is poised to propel the field forward, enabling deeper insights and more effective treatment strategies."
The Virtual Physiological Human initiative and computational physiology represent more than technical achievements—they signal a fundamental transformation in how we understand and treat heart disease. From AI models that detect hidden heart attacks to digital twins that simulate treatment outcomes, these approaches leverage the power of computation to personalize cardiovascular care.
While challenges remain in data security, accessibility, and validation 4 , the potential benefits are enormous. As these technologies mature, they promise to move cardiology from reactive treatment toward predictive, preventive, and personalized care—potentially saving millions of lives from the global burden of cardiovascular disease.
The journey from the stethoscope to the supercomputer has been long, but the most exciting developments in cardiovascular medicine may still be ahead of us, beating to the rhythm of algorithms rather than a heartbeat.