Digital-Physical Twins Converge to Transform Heart Failure Diagnosis and Prognosis

Heart failure affects more than 64 million people worldwide, yet the numbers conceal a deeper problem: the disease is extraordinarily personal. Each failing heart carries its own pattern of muscle remodeling, valve dysfunction, and disturbed coupling between the ventricle and the arteries, which is why two patients with identical ejection fractions can follow wildly different trajectories. A sweeping review published in Bioengineering & Translational Medicine argues that the field is now approaching a genuine convergence, one in which finite-element modeling, three-dimensional printing, and artificial intelligence are fused into a single translational framework the authors describe as a digital–physical twin of the patient’s heart. Rather than treating these technologies as separate tools, the review maps how they can feed one another: imaging feeds mechanics, mechanics feeds printing, printing validates models, and machine learning binds the whole loop into prediction.

The physiological anchor of the review is the recognition that heart failure is, at its core, a disease of disturbed mechanics. Changes in myocardial stiffness, fiber architecture, wall stress distribution, and chamber geometry drive the maladaptive remodeling that precedes irreversible decline. The authors emphasize that conventional metrics such as ejection fraction and chamber dimensions often miss the regional biomechanical abnormalities that determine therapeutic response. This is particularly true for heart failure with preserved ejection fraction, a phenotype increasingly understood as a disorder of diastolic dysfunction, microvascular impairment, and ventricular–vascular uncoupling rather than simple pump failure. At the molecular level, dysregulation of calcium-handling proteins including SERCA2a, the ryanodine receptor RyR2, and the sodium–calcium exchanger produces distinct cellular signatures in reduced- versus preserved-ejection-fraction disease, linking subcellular dysfunction to the macroscopic stiffness and impaired relaxation that clinicians measure at the bedside.

Finite-element modeling is the computational backbone of this convergence. By combining imaging-derived geometry with constitutive laws describing myocardial tissue behavior and physiologically realistic boundary conditions, these models can quantify regional stress and strain that no global clinical index can capture. The review illustrates the power of this approach through bioprosthetic heart valves, whose durability limits remain a major clinical problem. Under cyclic loading, valve leaflets undergo progressive geometric remodeling: after roughly 50 million cycles, equivalent to about two years of in vivo function, leaflets show permanent deformation concentrated in the central belly region, accompanied by collagen redistribution rather than outright fiber rupture. Time-dependent finite-element frameworks that iteratively update material parameters and remesh geometry across loading cycles can now reproduce this staged evolution from early remodeling to stress redistribution and eventual failure.

Collagen architecture emerges as a decisive variable in these simulations. Comparative finite-element studies incorporating isotropic fiber distributions, exogenously crosslinked bovine pericardial architectures, and native porcine aortic valve fiber architectures reveal striking differences in deformation behavior. Valves with isotropic or pericardial fiber arrangements deform relatively uniformly, with maximum in-plane Green–Lagrange strains of approximately 0.2, whereas valves carrying native fiber alignment display pronounced spatial heterogeneity, particularly within the leaflet belly. The modeling also shows that stress concentrations localize precisely where calcification and structural failure are observed experimentally, providing compelling evidence for mechanobiological coupling between cyclic stress exposure, collagen organization, and mineral deposition. For heart failure care, the relevance is indirect but important: these valve studies supply validated methods for quantifying how loading conditions, device–tissue interactions, and ventricular–vascular coupling shape remodeling trajectories.

Computational predictions, however, are only as credible as their experimental validation, and this is where three-dimensional printing enters the pipeline. What began as static anatomical replication from computed tomography datasets has matured into functional phantoms that reproduce both anatomy and mechanical behavior. Multimaterial printing technologies such as PolyJet can combine compliant soft-tissue regions with rigid calcified components in a single model, a capability the review highlights as particularly valuable for replicating aortic stenosis. When such patient-specific phantoms are coupled to pulsatile flow loops, they reproduce echocardiographic features and hemodynamic profiles of severe disease, with experimental measurements of transvalvular gradients and effective orifice area closely matching patient Doppler data. Fluid–structure interaction simulations based on the immersed boundary method, validated against pulse-duplicator experiments, show good qualitative agreement in leaflet kinematics, with residual discrepancies attributed to geometric variability between leaflets, differences in collagen architecture, and turbulent flow effects at physiological Reynolds numbers on the order of 20,000.

The clinical footprint of printed models already extends well beyond the bench. In pediatric and congenital heart disease, where complex spatial relationships among chambers, great vessels, and airways defy two-dimensional imaging, printed models have improved surgical strategy selection, interdisciplinary communication, and trainee education. In structural heart interventions, patient-specific models allow bench-top rehearsal of transcatheter valve deployment, atrial septal defect closure, and ventricular assist device placement, with quantitative prediction of paravalvular leak demonstrated using tissue-mimicking aortic root phantoms and biomechanically informed parameters such as the maximum bulge index. For adults with congenital heart disease complicated by heart failure, printed models have visualized systemic right ventricles, Fontan pathways, and surgically constructed baffles, directly supporting planning for advanced therapies including ventricular assist device implantation. The review stresses a critical caveat: geometric fidelity alone is insufficient, and mechanical fidelity must be characterized under use-specific loading conditions for a printed model to serve as a meaningful biomechanical surrogate.

Artificial intelligence completes the triad by extracting clinically actionable patterns from high-dimensional data. The review surveys a hierarchy from machine learning to deep learning, noting that rigorous separation of training, validation, and testing datasets is essential to avoid information leakage and inflated performance claims. Concrete applications are already impressive: an AI-enabled electronic stethoscope detected left-sided valve disease from heart sounds with area under the receiver operating characteristic curve values of 0.76 for aortic stenosis, 0.79 for mitral regurgitation, and 0.85 for composite left-sided disease, supporting point-of-care screening. Automated software quantifies aortic annular dimensions dynamically across the cardiac cycle, and the fully automated 4TAVR pipeline performs segmentation, landmark identification, measurement extraction, and report generation from computed tomography without any user interaction. Machine learning models predicting in-hospital mortality after transcatheter aortic valve replacement achieved an area under the curve of 0.92, with logistic regression outperforming more complex architectures. Natural language processing has exposed discrepancies between quantitative measurements and narrative conclusions in echocardiographic reports, while acoustic analysis of ventricular assist device sounds identifies complications such as significant regurgitation through harmonic amplitude and rotational speed features.

The review’s most sobering contribution is its candid assessment of evidence maturity, which it finds strikingly heterogeneous across the three technologies. Finite-element approaches remain predominantly validated in retrospective or simulation-based studies with limited prospective clinical testing, earning a low-to-moderate clinical readiness rating. Three-dimensional printed phantoms are widely used for in vitro testing and procedural rehearsal, yet direct evidence linking their use to improved patient outcomes is minimal, yielding the lowest readiness score. Artificial intelligence has achieved the broadest clinical integration, particularly in imaging and risk stratification, but most models rest on retrospective datasets without robust external validation. Strikingly, the authors identified no prospective studies directly linking patient-specific finite-element descriptors or phantom testing to hard endpoints such as all-cause mortality or heart failure hospitalization. They also caution that pooling accuracy metrics across these approaches is inappropriate because endpoints, datasets, and validation standards differ so substantially.

To close this gap, the review proposes a concrete research agenda built around candidate mechanical descriptors for heart failure phenotyping, including peak regional von Mises stress, end-systolic principal strain, strain heterogeneity index, transmural strain gradient, myocardial work density, annular displacement, and peak leaflet stress. Each descriptor is paired with a minimum validation pathway running from bench calibration through phantom testing to retrospective association and, ultimately, prospective outcome-linked trials. The authors acknowledge that finite-element outputs are sensitive to imaging quality, segmentation, constitutive assumptions, boundary conditions, and mesh resolution, and that harmonized reporting standards are prerequisites for multicenter comparison. Ethical considerations, including data governance, transparency, and algorithmic bias, must be resolved in parallel to earn regulatory and clinical trust.

The vision that emerges is not of any single technology replacing clinical judgment, but of an integrated ecosystem in which computational mechanics, physical validation, and data-driven prediction are jointly optimized for each patient. Imaging feeds patient-specific finite-element models; those models guide the fabrication of functional phantoms that experimentally confirm predicted stresses and flows; machine learning accelerates segmentation, builds surrogate models, and maps heterogeneous phenotypes such as preserved-ejection-fraction subtypes onto mechanistic descriptors. The authors conclude that these technologies are best viewed today as complementary components of a developing translational ecosystem rather than standalone clinical tools, with their greatest impact contingent on rigorous validation, standardized reporting, and heart-failure-specific clinical targeting. If prospective multicenter trials confirm the framework’s promise, the digital–physical twin could shift heart failure management from reactive measurement of global indices toward predictive, personalized, and mechanistically grounded care.

Subject of Research: Integration of finite-element modeling, 3D printing, and artificial intelligence for heart failure diagnosis and prognosis

Article Title: Mechanical‐medical convergence in heart failure: Artificial intelligence, finite‐element modeling, and 3D printing for diagnosis and prognosis

Article References: Sabrina, Q. N. E., Zobaer Shah, Q. M., Sohela, Q. N. E., Mousum, M. M. H., Chisty, M. M. U., & Shah, Q. M. A. (2026). Mechanical‐medical convergence in heart failure: Artificial intelligence, finite‐element modeling, and 3D printing for diagnosis and prognosis. Bioengineering & Translational Medicine, 11(5), Article e70156. https://doi.org/10.1002/btm2.70156

Image Credits: AI Generated

DOI: 10.1002/btm2.70156

Keywords: heart failure, finite-element modeling, 3D printing, artificial intelligence, digital twin, cardiac biomechanics, bioprosthetic heart valves, ventricular remodeling, machine learning, patient-specific modeling, transcatheter valve replacement, precision medicine