The research team built a patient-specific prediction framework and evaluated it on the OhioT1DM dataset, a widely used benchmark collected from people with Type 1 Diabetes wearing continuous glucose monitors. What sets the study apart is its commitment to minimal preprocessing. Many machine learning pipelines in diabetes research rely on elaborate cleaning, imputation and feature engineering stages that can be fragile in real-world deployment, where sensor dropouts and irregular meal logging are the norm. By keeping data preparation light, the Egyptian team aimed to test how well modern machine learning models perform when fed something much closer to the raw stream of clinical measurements that a real monitoring system would produce.

The experimental design was systematic. The researchers compared several machine learning models using standard regression metrics: Mean Absolute Error, which captures the average size of prediction mistakes; Root Mean Square Error, which penalizes large errors more heavily; and the coefficient of determination, or R², which measures how much of the variability in future glucose values the model actually explains. Crucially, they did not stop at statistical scores. They also applied Clarke Error Grid analysis, a clinical assessment tool developed in 1987 that judges predictions the way clinicians would, by asking whether a forecast error would lead to a harmless decision or a dangerous one. A model can look numerically decent while still making the kind of mistake that would trigger a wrong insulin dose, and the Clarke Error Grid is designed to catch exactly that.

Feature selection was explored through three combinations drawn from the literature. The simplest used blood glucose readings alone, testing how far a model can get on the glucose time series itself. The second added insulin-on-board, an estimate of how much previously injected insulin is still actively working in the body, together with meal information, since carbohydrates are the dominant driver of glucose excursions. The third configuration added physical activity data on top of everything else, acknowledging that exercise can send glucose plummeting or, in some cases, spiking, in ways that neither insulin nor food history fully explains. Each configuration was tested with a sliding input window of five historical time steps sampled at five-minute intervals, meaning the models looked back twenty-five minutes into each patient’s recent metabolic past.

Two prediction horizons were evaluated: thirty minutes and sixty minutes ahead. This choice reflects the practical realities of diabetes management. A thirty-minute forecast gives enough lead time for a pre-emptive snack or a delayed bolus, while a sixty-minute forecast is considerably harder because the effects of meals, insulin and activity compound over time, but it offers a much larger safety margin. The gap in difficulty between the two horizons is one of the central challenges in the field, and any framework that maintains accuracy at the longer horizon is doing genuinely useful work.

The centerpiece of the study is the Tri-Ensemble model, constructed by combining the predictions of the three strongest benchmarked models using simple averaging. Ensembling is a well-established strategy in machine learning: when individually strong models make somewhat different errors, averaging their outputs can cancel out idiosyncratic mistakes and produce a more robust overall prediction. The authors describe this aggregation choice as robustness-oriented, and the results supported that framing. The Tri-Ensemble demonstrated competitive predictive performance across multiple feature configurations and both prediction horizons, with statistically significant improvements observed in several of the experimental settings. In other words, the gains were not just artifacts of chance but held up under statistical scrutiny.

Beyond raw accuracy, the study contributes an interpretability layer. The researchers conducted an exploratory explainable AI analysis using SHAP, or SHapley Additive exPlanations, a technique borrowed from cooperative game theory that assigns each input feature a contribution value for every individual prediction. Rather than treating the model as a black box that spits out a glucose number, SHAP lets researchers open the box and see which variables pushed each forecast up or down, and by how much. The team applied this analysis over the complete available feature space, going beyond the three curated feature combinations used in the benchmarking experiments.

The SHAP analysis was performed patient by patient, and this patient-wise perspective is one of the study’s most interesting aspects. Type 1 Diabetes is famously heterogeneous: two people with the same diagnosis can have very different insulin sensitivities, eating patterns, activity levels and glucose dynamics. A feature that dominates predictions for one patient, such as insulin-on-board, may play a secondary role for another whose glucose is driven more by activity or meal timing. By examining feature importance patterns for each individual, the authors aimed to identify potentially informative variables that could guide feature engineering in future blood glucose prediction studies, effectively using the explainability tool as a discovery instrument rather than merely a compliance checkbox.

The significance of this work lies partly in its restraint. Rather than proposing an ever-larger deep learning architecture trained on heavily curated data, the study asks a more deployable question: how well can we predict glucose with modest models, light preprocessing and honest clinical evaluation? The answer, supported by the OhioT1DM benchmark and Clarke Error Grid analysis, is encouraging. A simple averaged ensemble of the strongest learners, given just twenty-five minutes of history and a handful of clinically meaningful signals, can deliver forecasts that are both statistically and clinically competitive. That combination of simplicity and rigor matters for anyone hoping to move glucose prediction out of the lab and into the pumps, pens and smartphone apps that patients actually use.

The road ahead, as the authors frame it, involves using the SHAP-derived feature relevance patterns to design smarter, personalized feature sets, potentially letting each patient’s model emphasize the signals that matter most for their own physiology. As continuous glucose monitoring spreads globally and the International Diabetes Federation projects continued growth in diabetes prevalence, tools that forecast blood glucose accurately, transparently and with minimal data fuss could become a quiet but transformative part of daily care. This study is a reminder that in medical machine learning, sometimes the most valuable innovation is not a bigger model, but a clearer view of what the model is actually seeing.

Subject of Research: Machine learning prediction of blood glucose in Type 1 Diabetes using the OhioT1DM dataset with SHAP-based explainability

Article Title: Blood glucose prediction in Type 1 Diabetes under minimal preprocessing using machine learning and exploratory SHAP analysis

Article References: Abdelaziz, D. M., Refaat, A. M., & Muhammad, S. T. (2026). Blood glucose prediction in Type 1 Diabetes under minimal preprocessing using machine learning and exploratory SHAP analysis. Neural Computing and Applications, 38(17), Article 706. https://doi.org/10.1007/s00521-026-12330-6

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12330-6

Keywords: Type 1 Diabetes, blood glucose prediction, machine learning, OhioT1DM dataset, Tri-Ensemble, SHAP, explainable AI, Clarke Error Grid, insulin-on-board, continuous glucose monitoring, patient-specific modeling, Neural Computing and Applications

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

Coarctation of the aorta, often abbreviated CoA, affects roughly one in several thousand live births and can remain clinically silent until hypertension or heart failure emerges in adulthood. Even after the narrowing is relieved by surgery or stenting, many patients continue to develop hypertension, diastolic dysfunction, and premature cardiovascular disease. Previous work by the same group and others has hinted that eliminating the trans-coarctation pressure gradient does not always normalize left ventricular function or aortic shear stress, suggesting that something beyond the pressure drop itself shapes long-term outcomes. The new study homes in on one candidate: post-stenotic energy dissipation, the irreversible loss of useful mechanical energy that occurs when high-velocity, disturbed flow exiting the narrowing collides with the complex three-dimensional anatomy of the descending aorta.

To dissect this phenomenon, the team analyzed a cohort of twenty patients with coarctation of the aorta. From computed tomography and magnetic resonance imaging, they extracted detailed anatomical features of the aorta, including the centerline topology—the curved path tracing the vessel’s midline—and the variation in cross-sectional area along the descending aorta. These geometric descriptors capture how sharply the aorta bends, how abruptly its caliber changes, and how the vessel’s axis deviates between the coarctation site and the segments immediately downstream. Rather than treating the aorta as a simple tube, the approach quantifies the actual anatomical landscape through which the jet of blood must travel after it exits the stenosis.

On top of the imaging analysis, the researchers built patient-specific multiscale computational simulations that couple three-dimensional aortic hemodynamics with models of cardiac function and systemic circulation. This framework allows the virtual blood flow to respond both to the local anatomy of each patient’s aorta and to the global behavior of the heart and arterial system. Using these simulations, the team quantified energy dissipation per unit volume in the descending aorta—a measure of how much of the blood’s mechanical energy is converted into heat and turbulence rather than forward motion. The multiscale design matters because energy loss in the aorta cannot be interpreted in isolation; it depends on the pressure and flow waveforms delivered by the left ventricle and on the resistance and compliance of the entire circulation.

The central finding is strikingly geometric. Energy dissipation per unit volume in the descending aorta correlates with the combined effect of two quantities: the kinetic energy available in the flow at the coarctation site, and the centerline tangent angle difference between the coarctation and the downstream descending aorta. In plain terms, it is not enough to know how fast the blood is moving as it leaves the narrowing; one must also know how much the vessel’s axis changes direction just downstream. A jet that exits the stenosis into a sharply angled or tortuous segment cannot follow the vessel wall smoothly, and the resulting flow separation, secondary motions, and turbulence dissipate far more energy than the same jet entering a well-aligned segment.

This angle-based insight reframes a familiar clinical observation. Post-stenotic dilatation—the widening of the aorta just beyond a narrowing—has long been recognized on imaging, and computational studies have linked it to the disturbed hemodynamics downstream of stenoses. The new results suggest that the same anatomical misalignment that promotes dilatation also acts as an energy sink, converting the kinetic energy generated by the ventricle into wasted, irreversible losses. Two patients with identical pressure gradients could therefore carry very different energetic burdens depending on the shape of their descending aortas, a distinction that the standard diagnostic metric is blind to.

Crucially, the study connects these downstream losses to the heart itself. The geometric and energetic parameters—the kinetic energy at the coarctation and the centerline angle difference—were found to significantly influence the left ventricular workload expended to compensate for post-stenotic energy loss. In other words, when the aorta’s geometry squanders the energy the heart delivers, the ventricle must do additional work to maintain adequate perfusion of the body. Over years, this chronic energetic penalty plausibly contributes to the left ventricular hypertrophy, diastolic dysfunction, and exercise intolerance documented in adults with repaired coarctation, outcomes that prior clinical studies have linked to abnormal arterial load indices even when resting blood pressure appears controlled.

The implications for clinical practice are substantial. Current guidelines from the American Heart Association and the European Society of Cardiology rely heavily on pressure gradients, imaging-derived indices of narrowing severity, and ventricular function to guide decisions about intervention. The new findings argue for incorporating anatomical and energetic metrics—centerline curvature, angle change, cross-sectional area variation, and computed energy dissipation—alongside pressure gradients when assessing disease severity and cardiac burden. Because the required inputs come from CT and MRI images that patients already undergo, the geometric analysis could in principle be folded into existing imaging workflows, and the computational framework builds on the group’s broader program of non-invasive, patient-specific diagnostic tools for valvular and vascular disease.

The work also resonates with a growing body of literature on cardiovascular energetics. Four-dimensional flow magnetic resonance imaging has been used to measure turbulent kinetic energy in aortic stenosis and coarctation, and studies of hypertrophic cardiomyopathy have shown that individualized computational modeling can reveal energetic inefficiencies invisible to conventional metrics. By identifying a simple, image-derived geometric predictor of energy loss, the McMaster team offers a bridge between high-fidelity simulation and practical clinical assessment. The method’s reliance on centerline morphology—something extractable from standard angiographic and tomographic data—makes it more translatable than approaches requiring specialized flow-sensitive sequences.

Limitations and next steps remain. The cohort comprised twenty patients, and the study, supported by NSERC Discovery and Alliance grants and computational resources from the Digital Research Alliance of Canada, will need validation in larger and more diverse populations, including patients before and after intervention. Longitudinal studies will be required to test whether geometrically driven energy dissipation predicts ventricular remodeling, hypertension, or clinical events over time. Still, the message is clear and potentially practice-changing: in coarctation of the aorta, the pressure drop across the narrowing is only the opening chapter of the story. The shape of the vessel that receives the jet—the bends, the angles, the tapering of the descending aorta—determines how much of the heart’s energy survives the journey, and how hard the ventricle must work to make up the difference. Reading the aorta’s centerline, the study suggests, may be as important as measuring its pressures.

Subject of Research: Geometric determinants of post-stenotic flow energy loss and left ventricular workload in coarctation of the aorta

Article Title: Geometric Determinants of Post-stenotic Energetics in Coarctation of Aorta: Linking Centerline Morphology to Downstream Energy Loss

Article References: Daeian, M., Karbassi, A., Ganame, J., Smith, S., & Keshavarz-Motamed, Z. (2026). Geometric Determinants of Post-stenotic Energetics in Coarctation of Aorta: Linking Centerline Morphology to Downstream Energy Loss. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04351-y

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04351-y

Keywords: aortic coarctation, hemodynamics, energy dissipation, centerline morphology, left ventricular workload, computational fluid dynamics, descending aorta, pressure gradient, patient-specific modeling, cardiac function, viscous dissipation, medical imaging

The biphasic plate, manufactured by 41 medical in Bettlach, Switzerland, represents a deliberate departure from decades of orthopedic philosophy. Early plate fixation pursued absolute stability after anatomic reduction, exploiting direct bone healing. Later designs, such as the Limited Contact Dynamic Compression Plate introduced by Gautier and Perren, minimized soft-tissue damage, while bridging plates allowed secondary healing through callus formation. Systems like the Less Invasive Stabilization System and retrograde nailing reduced complication rates, and dual plating has shown promise in complex distal femur fractures, although a systematic review by Tripathy and colleagues found no significant functional advantage over single plating and longer procedure times. The recurring problem is that very stiff constructs, often used in osteoporotic bone to permit early weight bearing, can shield the fracture from the mechanical stimulation that callus formation requires.

The biphasic concept answers this dilemma by building the flexibility into the plate body itself. The implant is thicker than a conventional locking plate, raising its overall stiffness and fatigue strength, but an integrated meander-shaped recess locally reduces stiffness and permits a controlled degree of elastic movement with mechanical amplitude limitation. Previous work by Hofmann-Fliri and colleagues, who adapted the plate to the ovine tibia in an animal model, demonstrated rapid and robust callus formation and improved construct strength compared with conventional locking plates, especially at higher weight-bearing levels. Epari and colleagues, testing the plate on bone substitute material with a 20-millimeter fracture gap under axial loads from 0 to 1000 newtons, found a bilinear stiffness response that delivered more consistent interfragmentary movement and greater implant strength, properties particularly relevant for distal femur fractures.

What remained missing was a bridge between these controlled characterizations and the conditions an actual patient experiences. The Saarland team closed that gap with a three-part methodology. In the laboratory arm, they obtained a cadaveric femur from a 75-year-old female body donor, created a 30-degree osteotomy mimicking an OTA/AO type 33A2 distal femur fracture, and fixed it with the biphasic plate using nine head-locking screws. The specimen was clamped in a custom-designed testing rig validated in earlier work, and a spindle-driven linear actuator applied axial compression in six steps of 0.25 millimeters each at a quasi-static rate of 0.25 millimeters per second.

Two measurement technologies made the experiment unusually informative. A camera system documented the state of the plate’s meander structure, showing it open during the flexible phase and closed once the rigid phase engaged. Meanwhile, an implantable strain sensor known as the AO Fracture Monitor, developed by the AO Foundation in Davos, was mounted directly on the plate with two screws tightened to 1.5 newton-meters. This wireless device, originally built to track bone healing progression at 10 hertz via a strain gauge and transmit data to the cloud via Bluetooth, recorded the plate’s strain response throughout loading. At the final compression step the force reached 235 newtons, at which point the meander closed and the plate transitioned from its flexible to its rigid phase, a transition the authors note becomes apparent at roughly 200 newtons.

The computational arm of the study was built to reproduce exactly this experiment. The cadaveric specimen was scanned with a Siemens Somatom Definition AS64 CT scanner at 120 kVp and 0.6-millimeter slice thickness, together with a calibration phantom, and metal artifact reduction was applied to the images. After segmenting cortical and cancellous bone in consultation with treating physicians, the team generated a finite element model using ten-node tetrahedral C3D10 elements in Abaqus. Bone material properties were derived from CT gray values through density-modulus relationships, producing 25 material classes ranging from 100 to 22,000 megapascals, while the stainless steel plate and bolts were assigned a Young’s modulus of 200 gigapascals and a Poisson’s ratio of 0.3. The meander gap was modeled with frictionless tangential and hard normal contact, and six simulation steps applied the experimental forces across the proximal femur.

Validation proceeded on two fronts. First, the simulated relative strain change across the six loading steps closely followed the smoothed curve recorded by the AO Fracture Monitor, with only minor deviations attributed to differences in force application, clamping, bone alignment, and node selection. Second, the team replicated the benchmark scenario of Epari and colleagues, applying axial loads from 0 to 1000 newtons and confirming the characteristic nonlinear load-interfragmentary movement response. In the near cortex the load rose steadily and then steepened beyond 0.2 millimeters of interfragmentary movement; in the far cortex the pronounced load increase occurred mainly beyond 1 millimeter. The von Mises stress distribution at 1000 newtons, spanning 0 to 850 megapascals, concentrated in specific plate regions and matched the earlier study’s findings, confirming that the model captured the biphasic mechanism itself.

The most striking results came from the in vivo arm. Four patient-specific models were generated from clinical CT data, incorporating each patient’s anthropometry, fracture situation, plate position, screw configuration, and individualized material properties. For one patient, sensor insoles and a motion capture system were used two days after surgery to record real gait under recommended weight-bearing conditions, and a musculoskeletal simulation in AnyBody derived the hip joint reaction force over time. The other patients’ loading profiles were drawn from the OrthoLoad database according to body weight and fracture side. Crucially, these load cases contained force components in all three anatomical planes, not just axial compression, and physiological proximal-distal forces during gait can reach approximately 2500 newtons, far exceeding the 1000-newton benchmark.

Under these realistic conditions, the stress picture inverted. Whereas axial loading distributed stresses centrally over the meander region, all four patient simulations placed maximum von Mises stresses along the lateral edge of the plate. The medial-lateral and anterior-posterior force components, together with the lever arm introduced by force transmission through the femoral head, superimpose bending and shear on axial compression, exactly as classical biomechanics described by Pauwels would predict. The authors emphasize that standardized axial test setups remain useful for implant characterization but cannot fully represent the multi-planar loading that governs implant behavior during walking, and that overly simplified in vitro or in silico setups risk missing clinically relevant stress concentrations.

The implications reach toward smarter, more individualized fracture care. Patient-specific simulations could identify implant regions exposed to elevated stresses during daily activities, informing future geometry optimization, working-length selection, and fixation strategies before clinical application. Combined with implantable sensors that continuously report plate strain as a surrogate for callus maturation, the approach points toward rehabilitation plans tailored to each patient’s actual loading environment. The team is candid about limitations: a single cadaver specimen, quasi-static axial loading capped at 1000 newtons, no biological or healing simulation, and possible segmentation errors mean the patient results should be read as model-based predictions rather than validated clinical outcomes. Even so, the study delivers what the authors identify as its key outcomes, validation and reproducibility, and demonstrates that only by integrating in vivo, in vitro, and in silico methods can the true mechanical life of a fracture implant be understood.

Subject of Research: Biomechanical evaluation of the distal femur biphasic plate using in vivo, in vitro, and in silico methodologies

Article Title: Integrated Study of the Distal Femur Biphasic Plate: Exploring In Vivo, In Vitro, and In Silico Methodologies

Article References: Integrated Study of the Distal Femur Biphasic Plate: Exploring In Vivo, In Vitro, and In Silico Methodologies. (n.d.). https://doi.org/10.1007/s10439-026-04357-6

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04357-6

Keywords: biphasic plate, distal femur fracture, finite element simulation, AO Fracture Monitor, interfragmentary movement, patient-specific modeling, bone healing, osteosynthesis, biomechanics, cadaveric testing, gait loading, smart implants

The research, led by Xue Liang and Marc-Philipp H. Schmid as co-first authors, with senior authorship shared by Bradley G. Leshnower of Emory University School of Medicine and Hai Dong of Emory University and the Georgia Institute of Technology, set out to build a patient-specific computational framework capable of predicting aortic geometry and diameter growth in uncomplicated type B aortic dissection. The team included investigators from Georgia Tech, Texas Tech University, Emory University, and the Aortic Institute at Yale-New Haven Hospital, where John A. Elefteriades, one of the world’s foremost aortic surgeons, contributed his clinical expertise. What makes their approach remarkable is its reliance on data that clinicians already collect in routine practice: standard CT imaging and blood pressure measurements. No experimental tissue testing, no specialized imaging protocols, and no extra procedures were required.

At the heart of the framework lies a sophisticated description of aortic wall mechanics known as the unified fiber distribution, or UFD, model. The wall of the aorta is not a simple balloon-like membrane; it is a layered, fiber-reinforced composite in which networks of collagen and elastin bear load in different directions. Traditional constitutive models often assume discrete fiber families, which can struggle to capture the full diversity of fiber orientations found in real arterial tissue. The UFD model, developed previously by Dong and colleagues, treats fibers as distributed continuously across orientations within the plane of the vessel wall, providing a unified mathematical description that bridges different regimes of fiber recruitment under load. This planar distributed-fiber formulation also accounts for a so-called second kind of Poisson effect, a distinctive feature of thin, fiber-reinforced membranes. Implemented as a user-defined material subroutine in the commercial finite-element software Abaqus, the model allowed the researchers to represent the mechanical behavior of each patient’s dissected aortic wall with a high degree of physiological realism.

The second key innovation was geometric. In a dissection, the aortic wall splits into two channels, a true lumen and a false lumen, separated by an intimal flap, creating an enormously complex three-dimensional architecture. To track how this living structure grows over time, the researchers needed a way to map material points of the aortic tissue consistently across serial scans. They developed a novel centerline-based algorithm that determines the local material coordinates of aortic tissues by anchoring them to the curved central axis of the vessel. This algorithm, written in MATLAB, assigns each element of the finite-element mesh a local coordinate frame that follows the tortuous, patient-specific anatomy, enabling the model to distinguish local circumferential and longitudinal directions and to apply growth laws in the correct anatomical orientation.

The workflow the team devised is elegantly symmetrical, combining inverse and forward analyses. For each of seven type B aortic dissection patients, the researchers obtained three serial CT scans captured at different time points, along with blood pressure data. Using the geometries from the first two scans, they performed an inverse finite-element analysis: rather than predicting forward from known parameters, they worked backward from the observed second geometry to calibrate the patient-specific growth parameters that must have driven the enlargement seen between the first and second scans. Because the wall stress-driven growth framework posits that the aorta remodels in response to the mechanical stresses it experiences, this inverse step effectively reads out each patient’s individual biological propensity for enlargement, encoded in personalized growth coefficients. Once calibrated, these parameters were fed into a forward growth simulation to predict the aortic geometry at the third time point, a moment never shown to the model during calibration.

The results were striking. When the predicted geometries were compared against the actual CT measurements at the third time point, the model’s forecasts proved remarkably accurate. Across all seven patients, the absolute percent error in the predicted maximum aortic diameter was less than 3.5 percent, and the error in the predicted mean diameter across all anatomical locations was under 4 percent. Given that clinically meaningful aortic growth is often measured in single millimeters, this level of accuracy suggests the framework captured genuine biomechanical information rather than simply averaging away individual variation. The proof-of-concept validation demonstrates that aortic enlargement in dissection is not an inscrutable biological lottery but, at least in part, a mechanically governed process that can be quantified from clinical data.

The significance of this work becomes clearer when viewed against the stubborn uncertainties of current clinical practice. Decades of outcome studies have shown that risk factors for failure of medical management in type B dissection include anatomic features, false lumen patency, and aortic dimensions, yet diameter alone remains an imperfect predictor, and growth rates vary enormously between patients. Previous computational efforts have used computational fluid dynamics and fluid-structure interaction analysis to probe hemodynamic predictors such as false lumen pressure and wall shear stress, often requiring specialized 4D flow MRI data. The new framework complements this body of work by shifting attention to the solid mechanics of the wall itself and by demanding nothing more than the imaging and pressure data that every dissection patient already generates during surveillance.

If validated at larger scale, the implications for patient care could be substantial. A calibrated digital twin of a patient’s aorta could, in principle, support individualized surveillance intervals, identifying early on which patients are destined for rapid enlargement and warranting closer monitoring or earlier thoracic endovascular repair, while sparing stable patients unnecessary anxiety and intervention. It could also help surgeons time intervention for maximal benefit, converting an evidence-based but reactive strategy into a proactive, predictive one. The researchers are careful to frame this as a proof of concept: seven patients is a modest cohort, and the growth parameters are calibrated from observed enlargement rather than true out-of-sample prediction over entirely unseen futures. Larger, multi-center validation studies will be needed before such simulations can inform clinical guidelines, which are currently codified in the 2022 ACC/AHA aortic disease guideline.

Nevertheless, the study represents a compelling convergence of biomechanics, imaging, and cardiothoracic surgery. By extending a stress-driven growth framework, embedding the unified fiber distribution model into patient-specific finite-element simulations, and inventing a centerline-based method to track tissue coordinates through serial scans, the Emory-Georgia Tech-Yale team has shown that the future geometry of a dissected aorta can be computed with clinically useful precision. The MATLAB code for the coordinate-assignment algorithm and the Abaqus implementation of the UFD model are available from the corresponding authors upon reasonable request, and the study was supported by the National Heart, Lung, and Blood Institute through grant R01HL155537 and by the Carlyle Fraser Heart Center. For the hundreds of thousands of patients worldwide living under the watchful uncertainty of a dissected aorta, the prospect of a simulation that reveals tomorrow’s anatomy from today’s scan is a vision of predictive medicine that has moved measurably closer to reality.

Subject of Research: Patient-specific finite-element prediction of aortic growth in type B aortic dissection using the unified fiber distribution model

Article Title: Patient-Specific Aortic Growth Prediction in Type B Aortic Dissection Using Finite-Element Simulation Based on the Unified Fiber Distribution (UFD) Model

Article References: Liang, X., Schmid, M.-P. H., Liu, M., Cebull, H. L., Zhang, M., Xu, S., Naeem, M., Oshinski, J. N., Elefteriades, J. A., Gleason, R. L., Jr., Leshnower, B. G., & Dong, H. (2026). Patient-Specific Aortic Growth Prediction in Type B Aortic Dissection Using Finite-Element Simulation Based on the Unified Fiber Distribution (UFD) Model. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-026-04379-0

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04379-0

Keywords: type B aortic dissection, aortic growth prediction, finite-element simulation, unified fiber distribution model, wall stress-driven growth, inverse finite-element analysis, patient-specific modeling, aortic aneurysm, optimal medical therapy, CT imaging, biomechanics, computational cardiology

The appeal of the approach lies in its simplicity and accessibility. Previous attempts to bring three-dimensional printing into breast reconstruction have generally depended on computed tomographic angiography data or dedicated surface-imaging platforms, both of which carry substantial costs and infrastructure requirements. CT-based workflows also expose patients to additional radiation when imaging has not already been obtained for surgical planning, and image segmentation demands specialized expertise that many centers cannot easily mobilize at the point of care. By contrast, the new technique uses the structured-light TrueDepth camera already built into an iPhone, capturing surface geometry through the ScandyPro application in a matter of minutes during an ordinary outpatient appointment.

Before applying the method to patients, the research team validated the accuracy of smartphone scanning against a professional-grade alternative. They fabricated a physical breast phantom from an open-source digital model using fused deposition modeling, then scanned it with two protocols. The smartphone protocol used ScandyPro on an iPhone 12, which reconstructs surfaces from the infrared depth data of the front-facing camera. The comparison protocol used a Canon digital single-lens reflex camera with a 24 millimeter lens, acquiring 100 to 150 photographs in circular passes at varying elevation angles and reconstructing them through photogrammetry in Agisoft Metashape. Fiducial markers provided correspondence points and a calibration bar of known length fixed the absolute scale, since structure-from-motion reconstructions are otherwise ambiguous in size.

The geometric comparison was carried out in CloudCompare using a two-stage registration procedure. A coarse alignment was first computed by manually selecting corresponding fiducial points, followed by fine registration with the Iterative Closest Point algorithm restricted to rigid-body motion, so that genuine geometric deviation was preserved rather than absorbed by the fit. The results were striking: smartphone scans showed a mean deviation of just 0.033 millimeters and a root-mean-square error of 0.697 millimeters, with individual scans ranging from 0.456 to 0.878 millimeters. Every smartphone scan outperformed the DSLR photogrammetry reconstruction, which recorded an RMSE of 0.954 millimeters. Both methods achieved sub-millimeter accuracy with negligible systematic bias, but the phone, remarkably, edged out the professional camera setup on this rigid test object.

With accuracy established, the team enrolled three patients prospectively, each of whom had previously undergone a unilateral mastectomy and was planned for autologous reconstruction. During a preoperative clinic visit, each patient’s native breast was scanned in the upright position with the smartphone application. The resulting surface model was exported in standard tessellation language and mirrored across the midline to represent the contralateral, pre-mastectomy breast. The investigators caution that because breast tissue is deformable and position-dependent, the mirrored upright scan serves as a patient-specific shaping reference rather than an exact replica of the supine anatomy the surgeon encounters in the operating room, but it provides a tangible target that visual estimation alone cannot.

The models were printed at Duke University’s Innovation Co-Lab on a Form 3B+ stereolithography printer using Elastic 50A resin, a flexible, semi-translucent material chosen for its malleability and its ability to let surgeons partially see through the template intraoperatively. Each print was oriented with its concave inner surface facing the build platform to avoid the cupping effect inherent to bottom-up stereolithography and to keep support material away from the functional outer surface. After support removal, the prints were washed in isopropyl alcohol, post-cured under ultraviolet light, and sterilized using the institution’s standard operating-room autoclave protocol, making them fully surgical instruments.

In the operating room, the templates served as physical guides for flap shaping. Two of the patients underwent unilateral reconstruction with bipedicled deep inferior epigastric perforator flaps, in which the entire abdominal flap was folded ninety degrees so that lateral tissue formed the superior breast border while contralateral tissue was de-epithelialized and folded beneath to provide lower-pole projection. The third patient underwent prophylactic mastectomy of the remaining breast followed by bilateral reconstruction with profunda artery perforator flaps, each shaped into a cone to maximize projection. In every case, the sterilized template was placed over the shaped flap to approximate symmetry with the native breast and to help the surgeon visualize the breast footprint and overall contour.

One particularly notable case demonstrated a capability that earlier single-use molds lacked: reusability. In a patient whose flap provided less tissue than the template represented, the mold still guided shaping despite the limited volume, and after the initial operation the same template was re-sterilized and used during a revision procedure to guide implant placement beneath the flap and improve projection. This adaptability addresses a recognized limitation of prior rigid, single-use molds and suggests the templates can follow a patient across the full reconstructive course. The authors note that when available tissue falls short of the template, the approach can be adapted through stacked flaps or hybrid techniques combining autologous tissue with implants, and that superficial wound-healing complications observed in one case were considered unrelated to the template workflow.

The researchers are careful to frame the study as a demonstration of technical feasibility rather than proven clinical benefit. With only three patients, the series cannot establish improvements in operative efficiency, symmetry, or patient-reported outcomes, and results were limited to subjective surgeon and patient satisfaction. The authors call for larger prospective studies incorporating blinded assessment against conventional visual judgment, operative and flap-shaping time, objective measurements of breast volume, shape, footprint, and postoperative symmetry, and validated patient-reported measures such as BREAST-Q scores. They also acknowledge that while smartphone capture eliminates the cost and radiation of CT-based workflows, the printing infrastructure itself remains an institutional investment, and the collection and storage of patient surface images raises privacy and regulatory considerations that must be managed within appropriate legal safeguards.

Nevertheless, the study points toward a democratization of surgical planning technology that could extend well beyond breast reconstruction. Three-dimensional printing has already transformed craniomaxillofacial surgery, prosthetics, surgical training, and patient education, and the authors anticipate a parallel emergence of soft-tissue applications as the technology matures. If subsequent studies confirm that a device carried in millions of pockets can capture anatomy with sub-millimeter fidelity and translate it into a sterilized operating-room tool at minimal incremental cost, the print-to-patient paradigm could reshape how reconstructive surgeons plan, practice, and personalize some of the most intricate operations in medicine, turning an everyday smartphone into an instrument of surgical precision.

Subject of Research: A smartphone-based 3D printing workflow for creating patient-specific intraoperative templates in autologous breast reconstruction.

Article Title: Print-to-patient: a novel 3D printing technique for autologous breast reconstruction

Article References: Morris, M. X., Barrow, B. E., Park, H. S., Rames, J., Haider, M., Feaster, J., Sekhar, S., Rattey, I., & Phillips, B. T. (2026). Print-to-patient: a novel 3D printing technique for autologous breast reconstruction. BMC Plastic and Reconstructive Surgery, 2(1), Article 25. https://doi.org/10.1186/s44452-026-00037-5

Image Credits: AI Generated

DOI: 10.1186/s44452-026-00037-5

Keywords: 3D printing, breast reconstruction, autologous reconstruction, DIEP flap, smartphone 3D scanning, surgical templates, plastic surgery, microsurgery, 3D imaging, surgical innovation, patient-specific modeling, reconstructive surgery