TAVI has transformed the treatment of aortic stenosis, the narrowing of the heart’s main outflow valve that affects millions of older adults worldwide. Instead of opening the chest, clinicians thread a collapsible biological valve through an artery and deploy it inside the native valve’s calcified frame. Yet the procedure is not always perfect. In a substantial fraction of patients, the new valve does not expand symmetrically against the hardened calcium of the aortic root, leaving gaps through which blood leaks backward, a complication called paravalvular leakage. Mild to moderate leakage affects between 7 and 40 percent of TAVI patients, and when moderate or severe it is strongly associated with increased late mortality. The standard remedy is balloon post-dilation, in which a non-compliant balloon is inflated inside the freshly implanted stent to push calcifications outward and force the valve frame into fuller contact with the vessel wall. It is performed in roughly 13 to 54 percent of cases depending on the registry.

Despite how common the maneuver is, computational models of TAVI have almost universally ignored it. A recent scoping review cited by the authors examined 40 studies that used numerical simulation to predict TAVI outcomes and found that not a single one modeled balloon post-dilation. Two prior efforts, by Li and colleagues and by Akodad and colleagues, attempted to simulate the ballooning step, but neither calibrated the balloon’s material properties through physical testing nor validated their predictions against real post-operative images. That gap matters because the decision to perform post-dilation is a delicate risk-benefit calculation: the maneuver can enlarge the valve area, lower pressure gradients, and reduce leakage, but it carries risks including annular rupture, valve migration, stroke, conduction disturbances, and damage to the delicate leaflets of the new valve.

To close that gap, the Milan-based team reconstructed a True Dilatation non-compliant balloon, a commercially available device from BD, using reverse engineering. They photographed a physical balloon at high resolution, measured its dimensions with a digital caliper, and processed the images in ImageJ to extract precise geometry, then built a computer-aided design model reproducing the device’s 85-millimeter total length, 45-millimeter working length, and 21-millimeter maximum diameter, later scaling it to the 22, 24, and 26 millimeter sizes actually used in patients. The balloon wall was represented as a shell just 0.3 millimeters thick, and the model was meshed with triangular shell elements averaging 0.8 millimeters, matching the refinement of the stent mesh to keep contact calculations stable.

Crucially, the team did not simply assume the balloon’s mechanical behavior; they measured it. Specimens cut from real balloons were stretched in a bioreactor equipped with a precision load cell, in both the longitudinal and circumferential directions, through six full loading-unloading cycles. The resulting stress-strain curves revealed a Young’s modulus of approximately 600 megapascals, with nearly identical values in both directions, indicating that the material behaves as an essentially isotropic elastic solid. To validate this finding, the researchers inflated an intact 21-millimeter balloon with water in a custom 3D-printed rig, raising the pressure in half-atmosphere steps up to four atmospheres while measuring the external diameter with a caliper. When they replicated this experiment in the virtual world using their 600-megapascal material model, the simulated pressure-diameter curve matched the experimental measurements with a coefficient of determination of 0.97, with the closest agreement near the nominal working pressure of three atmospheres.

With the balloon model calibrated and validated, the researchers integrated it into the final configurations of patient-specific TAVI simulations previously developed by the same group. Four patients who underwent TAVI at Humanitas Research Hospital between 2017 and 2024 were retrospectively included, all of whom had received self-expanding valves and post-dilation, and all of whom had post-operative CT scans available for comparison. In the simulations, the crimped balloon was placed inside the virtually implanted stent, inflated to its nominal pressure to push calcifications against the aortic wall and fully expand the Nitinol frame, then deflated to capture any elastic recoil. Two of the four patients also carried mechanical mitral valve prostheses, which the models explicitly represented as rigid bodies to capture potential device-device interference. All computations ran on an explicit finite element solver, LS-DYNA, using damping and selective mass scaling to keep energy ratios under five percent and maintain a constant time increment of one microsecond.

The validation against clinical imaging delivered striking results. Comparing the simulated final stent geometry with that segmented from post-operative CT scans, the average error in orifice area across three cross-sectional planes was just 1.55 percent, with a standard deviation of 1.26 percent, while the error in stent eccentricity at the level of the left ventricular outflow tract averaged 1.54. More telling was the comparison with simulations that omitted post-dilation: including the ballooning step reduced the orifice area error from 3.1 percent to 1.6 percent, a 53 percent improvement, and slashed the eccentricity error from 7.4 percent to 1.5 percent, an 80 percent improvement. Centerline analysis, which measures how closely the simulated stent axis follows the real one, found a mean deviation of only 0.386 millimeters across all patients.

The researchers also explored what the geometry changes mean physiologically. Using a simplified geometric surrogate for paravalvular leakage based on peri-prosthetic flow volume, they found that post-dilation reduced the leakage-prone space in three of the four patients, suggesting improved sealing between device and wall. The fourth patient, who started with a very low baseline flow volume, showed essentially no change, a reminder that the benefits of ballooning depend heavily on each patient’s unique anatomy. The authors explain that the key mechanism is not material damage but the redistribution of contact constraints: the balloon displaces calcified deposits, which alters the available space for stent expansion, so after deflation the Nitinol frame settles into a new equilibrium that would never be predicted by a deployment-only simulation. Because calcification patterns differ so dramatically between patients, the effects of post-dilation can be non-uniform and even counterintuitive, which is precisely why patient-specific modeling is needed.

The implications reach beyond academic curiosity. The authors argue that a validated virtual post-dilation capability could support pre-operative planning in which clinicians first examine the predicted post-deployment configuration of the valve, checking stent expansion, eccentricity, and apposition to the annulus, and then virtually test whether ballooning with different balloon sizes would deliver meaningful improvement at an acceptable safety margin. Such digital twins could help operators decide when corrective ballooning is likely to help, and when it merely adds risk. The study does carry limitations: only four patients were analyzed, partly because routine post-operative CT imaging is not standard practice, and the models do not simulate aortic wall rupture or calcification fracture, nor do they compute actual blood flow for leakage quantification. Still, the researchers see this work as a clear step toward credible, clinically deployable in silico tools. Notably, a review of the field found that only about 10 percent of TAVI simulation studies validated their results against post-implant CT scans at all. By calibrating every component against benchtop experiments and every prediction against real clinical outcomes, this study sets a new benchmark for what rigorous validation in computational cardiology should look like, and it brings the vision of personalized, simulation-guided structural heart intervention measurably closer to the operating room.

Subject of Research: Patient-specific computational modeling and validation of balloon post-dilation in transcatheter aortic valve implantation

Article Title: In Silico Modeling and Validation of Post-Dilation in TAVI Patients

Article References: Perri, L. M., Grossi, B., Fregona, V., Barati, S., Barbieri, L., Tumminello, G. D., Carugo, S., Bianchi, E., De Stefano, P., Berti, F., Cozzi, O., Condorelli, G., Stefanini, G., Dubini, G., Rodriguez-Matas, J. F., Migliavacca, F., & Luraghi, G. (2026). In Silico Modeling and Validation of Post-Dilation in TAVI Patients. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04325-0

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04325-0

Keywords: TAVI, balloon post-dilation, finite element analysis, patient-specific simulation, paravalvular leakage, aortic stenosis, Nitinol stent, computed tomography, in silico medicine, biomedical engineering, model validation, digital twins

The paper, written by Marco Viceconti of the Department of Industrial Engineering at the University of Bologna, introduces what the author calls the 7S Framework, a seven-step general procedure for evaluating the credibility of any quantitative estimate, whether it originates from a sensor, a statistical model, or a predictive simulation. The motivation is practical rather than purely philosophical. In fields such as in silico medicine, where computational models increasingly inform clinical decisions and regulatory submissions, a new generation of tools refuses to sit neatly within any one of the traditional categories. In silico-augmented clinical trials, physics-informed machine learning predictors, and machine learning models trained on synthetic datasets all blend measured data, statistical inference, and causal prediction into single estimators, leaving established credibility frameworks unable to cover them cleanly.

To build the framework, Viceconti begins with a conceptual scaffolding often described as the pyramid of knowledge. In this picture, observation lifts raw signals produced by a system of interest into data; annotation with metadata about who, what, where, and when lifts data into information; modelling correlations lifts information into tentative causal beliefs; and subjecting those beliefs to falsification experiments lifts them into actionable knowledge. Quantitative information, in this scheme, is an annotated set of values whose metadata specifies the domain of information, everything sender and receiver must know for the values to be meaningful, but without the causal “why” that would elevate it to knowledge. New information can be created by measurement, which converts signals into data; by inference, which derives new information from existing information; or by prediction, which uses causal knowledge to generate estimates of quantities that were never observed.

From these foundations, the author generalises a vocabulary that statistics normally reserves for inference. The target quantity is the estimand, the value produced is the estimate, and whatever produces it, be it a thermometer, a regression, or a finite element model, is an estimator. Credibility is then defined with a deliberately demanding definition: the minimum accuracy with which an estimator recovers the true value of the estimand across the entire information space, the bounded multidimensional region defined by all the observable quantities on which the quantity of interest depends. Accuracy itself borrows from metrology, where trueness captures systematic error and precision captures random error, combined into a normalised class of accuracy averaged over repeated estimations.

A crucial insight of the paper is that credibility expectations come in three levels, and that these levels are properties of the intended use rather than of the type of estimator. Level 1 credibility demands only that an estimate fall within a predefined uncertainty band around the true value, appropriate when knowing the order of magnitude suffices. Level 2 requires that the average of repeated estimates match the true value in a statistical sense, as when comparing central properties of populations. Level 3 demands local accuracy at every validation point, the standard for subject-specific models intended to predict individual outcomes. A predictive model can therefore be L1, L2, or L3 credible depending on whether it is meant to capture a scale, a population mean, or a person-specific value, and the same hierarchy applies to measurement and inference.

Because brute-force induction, measuring the error at an effectively infinite number of points, is practically impossible and not even theoretically guaranteed for all estimators, the framework follows the strategy shared by metrology, statistics, and VVUQ: decompose the estimation error into its sources and check that each component behaves as theory predicts for a well-behaved estimator. The seven steps formalise this logic. Step S1 defines the context of use and the acceptable error threshold, the maximum error that still leaves the information useful for the decision it must support, and sets this against the limits of validity imposed by the physics of the phenomenon. Step S2 establishes the source of true values, insisting on measurement chains at least an order of magnitude more accurate than the threshold. Step S3 quantifies estimation error through controlled experiments sampled across the solution space. Step S4 identifies the sources of error, which the paper groups into approximation, aleatoric, and epistemic contributions. Step S5 decomposes the overall error among these sources, sometimes requiring special experiments in which all but one error source is excluded. Step S6 critically reviews whether each error component is distributed as expected. Step S7 examines robustness to biases that could emerge in routine use, including applicability, the guarantee that real-world inputs never stray beyond the validity limits explored during assessment. Transparency throughout, particularly about which error sources are considered and how they are separated, is flagged as essential.

The paper demonstrates the framework on seven use cases drawn from the author’s research programme, three of which are summarised in detail. The first concerns strain gauge measurements of bone tissue deformation, used to validate finite element models that predict fracture. The context of use fixes an error threshold derived from the strain difference used to determine fracture, attenuated by two orders of magnitude to account for the chain of inference. True values come from beam-theory calculations on machined aluminium alloy specimens corrected for curvature error; trueness is computed as a root-mean-square error; normality tests confirm the expected distribution of random and systematic errors; and repeated tests on bone specimens establish applicability.

The second case applies the framework to the BBCT-Hip predictor, a biophysical model that estimates mechanical strains in a patient’s bone from a calibrated computed tomography scan. Here the error threshold is set at two percent of the cortical bone failure strain in compression, about 146 microstrain. True values come from strain gauge measurements on carefully preserved cadaveric femurs. The model’s predictions carry numerical, aleatoric, and epistemic errors, and the VVUQ procedure separates them, with the numerical component required to be negligible, the aleatoric component normally distributed with a near-zero mean, and the epistemic component showing a root-mean-square error close to zero. Applicability is probed by exhaustive experiments spanning inter-subject variability and all relevant loading conditions.

The third case is the most forward-looking: assessing a synthetic cohort inferred from a real clinical cohort of elderly patients at risk of hip fracture. The goal is to run in silico trials on virtual populations far larger than any experimentally collected cohort could be, comparing central properties such as means and medians of feature distributions and model predictions. The error thresholds are tied to the measurement and prediction accuracy of each quantity; epistemic error vanishes because the synthetic data are generated by inference, leaving aleatoric error from measurement uncertainty and numerical error from the interpolation functions, which must be shown negligible by sensitivity analysis. Applicability restricts use of the synthetic cohort to the portion of the information space actually sampled by the clinical data.

Viceconi is careful about scope. For estimators that fall squarely into the classical categories, he recommends continuing to use metrology, statistics, or VVUQ, which are more mature and widely accepted within their communities. The 7S Framework is positioned as a supplement for the growing class of hybrid estimators that do not fit anywhere: in silico-augmented trials that inject model predictions into Bayesian device trials, physics-informed neural networks that encode biomechanical law inside learned predictors, synthetic datasets generated to sidestep privacy constraints, and machine learning surrogates trained to replace expensive biophysical simulations. The framework was also applied to cases covering fracture-risk prediction and machine learning surrogates of computational models, and the author reports that it proved effective, sufficiently general, and sensitive to the subtle differences in what credibility means for each information type. Limitations acknowledged include the restriction of the exposition to single scalar quantities, although extension to vectors and time-dependent quantities requires only adding a norm, and the framework’s status as a generalisation rather than a replacement of existing practice. As computational medicine pushes further into regulatory territory, the stakes of getting credibility assessment right rise accordingly, and a shared epistemological vocabulary spanning measurement, inference, and prediction may prove to be exactly what regulators, developers, and clinicians need.

Subject of Research: A general seven-step framework for assessing the credibility of measured, inferred, and predicted quantitative information in computational medicine

Article Title: Assessing the Credibility of Quantitative Information: A General Framework

Article References: Viceconti, M. (2026). Assessing the Credibility of Quantitative Information: A General Framework. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04367-4

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04367-4

Keywords: credibility assessment, quantitative information, metrology, statistical inference, verification validation and uncertainty quantification, in silico medicine, machine learning predictors, synthetic data, 7S Framework, biomedical engineering, clinical decision-making, predictive models