Annals of Biomedical Engineering

Rogayan, Danilo V.

doi: 10.1007/s10439-025-03822-ypmid: 40794214

Biomedical engineering education in the Philippines is gaining momentum as universities and research institutions strive to integrate engineering and medical sciences to address local healthcare challenges. This article explores the current landscape of biomedical engineering programs, focusing on curriculum design, research initiatives, industry linkages, and graduate employability. It highlights the efforts of key institutions and identifies gaps in policy and professional recognition. Recommendations are proposed to align local programs with global standards and foster innovation through clinical integration, technopreneurship, and industry collaboration.

Daly, Timothy

doi: 10.1007/s10439-025-03879-9pmid: 41060347

Like writing, reading is thinking, too. Academics must promote the reading of scholarly literature, protect academic literacy and resist delegating this essential process to large language models so as to cultivate their own intellectual virtues and those of their students.

Langer, Nina; Escher, Andreas; Ozturk, Caglar; Stephens, Andrew F.; Roche, Ellen T.; Granegger, Marcus; Kaye, David M.; Gregory, Shaun D.

doi: 10.1007/s10439-025-03821-zpmid: 40853579

Heart failure with preserved ejection fraction (HFpEF) is a growing health problem worldwide, accounting for half of all heart failure cases. HFpEF patients present with diverse underlying causes and symptoms, making diagnosis and treatment challenging. Current pharmacological therapies are inadequate, while approved device-based therapies have shown limited success due to patient heterogeneity. This underscores the need for improved pre-clinical models, critical for guiding the design and development of effective therapeutic devices. This paper presents an overview of current pre-clinical HFpEF models, including in-silico, in-vitro, ex-vivo, and in-vivo approaches, aimed at advancing the understanding of HFpEF physiology and the development of device-based therapies. We examined each model's ability to replicate key HFpEF characteristics, discuss their respective strengths and limitations, and highlight their role in supporting the creation of clinically relevant solutions. Additionally, the potential of emerging advancements is explored.

Yu, Long; Zhu, Deyuan; Cai, Yunhan; Fang, Yibin; Wang, Shengzhang

doi: 10.1007/s10439-025-03810-2pmid: 40690121

PurposeThe cerebral anterior circulation arteries are the primary vessels supplying blood to the brain, and severe stenosis in these arteries can lead to ischemic stroke. Traditional imaging-based methods for assessing stenosis severity primarily focus on the diameter reduction at the narrowest point, which often fails to accurately reflect the functional severity of arterial stenosis. The FFR is considered the gold standard for assessing coronary artery stenosis. This study aims to revisit the original definition of FFR and develop a method for functionally assessing stenosis in the cerebral anterior circulation arteries.MethodsPatient-specific artery models representing both stenosed and post-repair conditions were generated based on clinical data. Numerical simulation models were then developed, and BFFR was calculated as an assessment metric. The accuracy of the numerical simulation model was validated through in vitro experiments.ResultsThe average bifurcation coefficient across the 9 cases was 2.82. The numerical simulation results for all cases were consistent with the clinical CTP measurements, accurately distinguishing the relative blood flow between the left and right arteries. The mean BFFRmin\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_{min}$$\end{document} for patients with mild stenosis was 1.53 times higher than that of patients with moderate and severe stenosis. The relative error between the total flow obtained from the numerical simulations and the experimental measurements was less than 3%.ConclusionCompared to traditional diameter stenosis rates, BFFR offers a significant advantage in evaluating cerebral artery stenosis. Furthermore, the numerical simulation model developed in this study demonstrated high accuracy.

Corvo, Alessandra; Avril, Stéphane; Aliseda, Alberto; Haulon, Stéphan; Chassagne, Fanette

doi: 10.1007/s10439-025-03806-ypmid: 40696220

Purpose:Fenestrated endovascular aneurysm repair (fEVAR) is widely used to treat complex abdominal aortic aneurysms, requiring renal artery stenting. However, complications such as occlusion can occur within the renal arteries. This study examines the effect of respiration-induced deformations, using patient-specific models and computational simulations. By investigating the impact of stenting and breathing, this research aims to improve surgical pre-planning and minimize EVAR complications.Methods:Pre-EVAR geometries from CT scans were segmented and meshed. Respiratory-induced displacements were applied to the segmented ends of the renal arteries to simulate breathing. The deployment process was achieved via balloon expansion, testing bridging stent-grafts with different lengths. To evaluate the accuracy of the workflow, simulated results and post-op CT scans were compared using centerline analysis, measuring morphological differences between the patient-specific models and the actual patients.Results:Numerical simulations accurately predicted renal artery movement during respiration, aligning with in vivo measurements. Simulated stent-graft configurations closely matched post-EVAR CT scans. Stent-graft protrusions into the aortic lumen were within the expected range, indicating correct positioning. Longer stent-grafts constrained renal artery movement, affecting branching angle changes, while shorter grafts had a less pronounced impact.Conclusions:Our novel digital twin model accurately simulates fEVAR procedures, including the deployment of renal bridging stent-grafts. Numerical simulations capture the bending of the renal arteries during breathing and their morphological changes following stenting in the post-operative configurations. Future research aims to expand the patient cohort and combine the solid mechanics simulations with CFD analysis.

Frostelid, Vetle Christoffer; Wajdan, Ali; Villegas-Martinez, Manuel; Hammersboen, Lars-Egil R.; Espinoza, Andreas; Grymyr, Ole-Johannes H. N.; Halvorsen, Per Steinar; Elle, Ole Jakob; Remme, Espen W.

doi: 10.1007/s10439-025-03828-6pmid: 40824372

Assessment of the contractile function of the heart typically requires resource demanding techniques, such as invasive left ventricular catheterisation or intermittent medical imaging, and is therefore not readily available for continuous clinical or remote monitoring. Measurement of heart wall motion by use of an epicardially attached three-axis accelerometer has emerged as a promising tool for monitoring cardiac function; however, previous methods have often underutilised the spatial and temporal information contained in the measured signals, potentially limiting its clinical reliability. This work reconstructs the position of an epicardial accelerometer in 3D space in order to enable extraction of indices of cardiac function in a Lagrangian frame of reference. The standard deviation of Lagrangian acceleration throughout a heartbeat, σAcc\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\sigma _{Acc}$$\end{document}, is introduced as a novel surrogate indicator of contractility as changes in σAcc\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\sigma _{Acc}$$\end{document} correlated strongly with changes in the maximum rate of change in left ventricular pressure in animal data (n=29) spanning a variety of haemodynamic conditions. The reported findings of this proof-of-principle study may represent a first step towards long-term monitoring of contractile function and expands on the current repertoire of use for epicardially attached accelerometers as versatile, continuous, and autonomous monitoring devices.

Al Bannoud, Mohamad; Martins, Tiago Dias; de Lima Montalvão, Silmara Aparecida; Annichino-Bizzacchi, Joyce Maria; Filho, Rubens Maciel; Maciel, Maria Regina Wolf

doi: 10.1007/s10439-025-03837-5pmid: 40965812

PurposeThe solution of the system of equations that model the coagulation cascade enables the determination of thrombin production, which is related to blood clot formation and thrombosis. However, traditional models often overlook clinical and hematological variables due to modeling challenges or incomplete understanding. Mathematical models of blood coagulation cascade are typically generalist, presenting limited accuracy. This study aimed to incorporate patient-specific and hematological data into the kinetic parameters of the coagulation cascade to generate individualized thrombin curves and predict the recurrence of venous thromboembolism.MethodsA sensitivity analysis identified the most influential kinetic parameters for thrombin production. These parameters were adjusted using a model hybrid combining an artificial neural network with a system of ordinary differential equations optimized via a genetic algorithm. The dataset is split into two subsets to prevent data leakage.ResultsEight kinetic rates were identified as the most sensitive, particularly those related to factor V activation and thrombin–antithrombin III complex formation. Factors such as anticoagulant use, smoking, pulmonary embolism, and factor V Leiden mutation significantly impacted the kinetic parameters. The model presented an AUC of 0.9941 and an accuracy of 0.9872.ConclusionThe influence of these input variables on the kinetic parameters and thrombin production aligned with their known effects as risk factors reported in the literature. Adjusting the kinetic parameters individualized the model response, providing a clear cutoff point for thrombosis classification based on thrombin production. With further validation, this model could assist in diagnosing and prognosticating thrombosis and identifying new therapeutic targets to regulate thrombin production.

Yıldırım, Canberk; Uçak, Kağan; Madayen, Ali; Gölcez, Tansu; Ertürk, Hakan; Baran, Özgür Uğraş; Pekkan, Kerem

doi: 10.1007/s10439-025-03834-8pmid: 40926069

PurposeThe design and development of ventricular assist devices have heavily relied on computational tools, particularly computational fluid dynamics (CFD), since the early 2000s. However, traditional CFD-based optimization requires costly trial-and-error approaches involving multiple design cycles. This study aims to propose a more efficient VAD design and optimization framework that overcomes these limitations.MethodsWe developed a system- and component-level ventricle assist device optimization approach by coupling a lumped parameter cardiovascular physiology model with parametric turbomachinery, volute design, and blade path generation packages. The framework incorporates pump hydrodynamic losses and is validated against experimental data from six distinct blood pump designs and CFD simulations. The optimization framework allows for the specification of both physiology-related and device-related objective functions to generate optimized blood pump configurations over a large parameter space.ResultsThe optimization was applied to the U.S. Food and Drug Administration (FDA) benchmark blood pump as the baseline design. Results showed that an optimized FDA pump, maintaining the same cardiac output and aortic pressure, achieved a ~ 32% reduction in blade tip velocity compared to the baseline, resulting in an ~ 88% reduction in hemolysis. Additionally, an alternative design with a 40% reduction in blood-wetted area was generated while preserving the baseline pressure and flow.ConclusionThe proposed optimization framework improves device development efficiency by shortening the design cycle and enabling hydrodynamically optimized pumps that perform well across diverse patient hemodynamics. The optimized pump designs are available as open-source resources for further research and development.

Srikanchana, Rujirutana; Samuel, David; Powell, Jacob; Pickett, Treven; DeGraba, Thomas; Sours Rhodes, Chandler

doi: 10.1007/s10439-025-03853-5pmid: 40987945

PurposeThe aim of this research was to assess the potential for machine learning to predict clinically significant patient improvement during a four-week interdisciplinary Intensive Outpatient Program (IOP) for traumatic brain injury (TBI) at the National Intrepid Center of Excellence (NICoE).MethodsAssessment of brain injury characterization and outcomes were measured in 790 active duty service members at the NICoE, Walter Reed National Military Medical Center Bethesda Maryland. Demographic and self-reported measures of posttraumatic stress, depression, anxiety, post-concussion symptoms, and sleep were assessed upon admission. Total scores and symptom cluster scores for self-report measures were calculated. Clinically significant improvement from pre- to post NICoE IOP was operationally defined as clinically significant changes in posttraumatic stress and post-concussion symptoms. Two datasets were created: one including demographics and total scores on self-report measures and one including demographics, total scores, and symptom cluster scores for relevant self-report measures. Extreme gradient boosting (XGBoost) models were trained to predict group identification (clinically significant improvement vs. not significant improvement), where a binary logistic objective function is used to minimize the log loss between the predicted probabilities. Model performance and feature ranking were then evaluated on test datasets.ResultsThe performance and feature importance of two models to predict group identification were evaluated, where the model including only demographics and total self-report measures performed with an AUC of 75% with the accuracy of 68%, compared to the model incorporating demographics and symptom cluster measures improved the AUC to 79% with 72% accuracy. The top five features contributing to the model with symptom clusters included the posttraumatic stress arousal, avoidance, and reexperiencing sub-scores, education, and postconcussive symptoms cognitive sub-score.ConclusionUtilization of the XGBoost models demonstrated acceptable discrimination for determining key factors associated with clinically significant improvement for SMs following participation in an interdisciplinary IOP using demographics and self-report measures. Severity of posttraumatic stress symptoms upon admission was the greatest predictors of clinically significant improvement in this model of care. Incorporating ML algorithms into clinical care is a precision medicine approach that may accurately predict treatment efficacy leading to improved healthcare resource allocation and patient outcomes.

Li, Lingli; Li, Hongxiao; Jiang, Miaowen; Fang, Jing; Ma, Ning; Yan, Jianzhuo; Zhou, Chen

doi: 10.1007/s10439-025-03838-4pmid: 40986253

Background and PurposeEarly Neurological Deterioration (END) following intravenous thrombolysis (IVT) highlights potential risks in current management strategies for acute ischemic stroke. Early identification of at-risk patients could enhance treatment efficacy. This study aims to develop an advanced AI predictive model that improves accuracy in forecasting END while ensuring interpretability for clinical application.MethodsThis prospective cohort study included 970 patients with acute ischemic stroke who underwent IVT. Data from 365 patients were used for model development and internal validation, while data from 605 patients were utilized for external validation. Five machine learning models were developed and compared using evaluation metrics such as accuracy and AUC. Feature selection and model optimization were performed using the XGBoost algorithm and SHapley Additive exPlanations (SHAP) method, resulting in the StrokeENDPredictor-19 model.ResultsAmong the five models, XGBoost demonstrated superior performance with an internal validation accuracy of 91% (AUC = 0.96) and external validation accuracy of 90% (AUC = 0.95). Notably, this study established cutoff values for critical clinical features, providing quantifiable reference standards for practical applications.ConclusionThe StrokeENDPredictor-19 model offers neurologists a valuable tool for forecasting the likelihood of END in patients receiving IVT therapy, thereby supporting more precise clinical decision-making.