Computer Methods And Programs In Biomedicine[JOURNAL]
Sun
200 papers
A new computational pipeline for multicompartment modeling of liver perfusion.
Chetoui MA, Beaurepaire P, Morvan M … +1 more , Vacavant A
Comput Methods Programs Biomed · 2026 May · PMID 41806547 · Publisher ↗
OBJECTIVE: Multicompartment porous media models are increasingly used to describe the hierarchical organization of hemodynamics in highly perfused tissues. The objective of this study is to develop a patient-specific mul...
OBJECTIVE: Multicompartment porous media models are increasingly used to describe the hierarchical organization of hemodynamics in highly perfused tissues. The objective of this study is to develop a patient-specific multiscale finite element model for liver perfusion that accounts for interactions between large vessels and the microvasculature. METHODS: A multiscale framework is proposed that couples a 1D transport model for large hepatic vessels with a 3D multicompartment porous media model for microvascular perfusion. The geometry of the major hepatic vessels is explicitly included, enabling blood exchange between the vascular and porous compartments to be represented through interface flux conditions that prescribe both magnitude and direction of flow. A fully coupled numerical strategy is employed to ensure consistent interactions between scales and to improve computational efficiency and accuracy. RESULTS: The simulations produce physiologically consistent pressure distributions and perfusion patterns. The coupled approach captures key features of hepatic hemodynamics and improves numerical robustness compared to sequential coupling strategies. CONCLUSION: The proposed model provides a coherent multiscale description of liver perfusion by integrating patient-specific vascular geometry within a fully coupled framework. This approach represents a step forward in liver perfusion modeling, with potential applications in patient-specific medicine.
Integrating machine learning of bulk and single cell RNA data to characterize immune mechanism of m6A related mitophagy genes in malignant gastric cancer.
Yan L, Sun J, Feng Y … +3 more , Yang X, Xu C, Wang J
Comput Methods Programs Biomed · 2026 May · PMID 41797008 · Publisher ↗
BACKGROUND AND OBJECTIVE: Gastric cancer is a heterogeneous and complicated epithelial cancers. Chronic H. pylori and EBV infection, as well as intestinal microbiota exposure make gastric cancer encountered a complex tum...
BACKGROUND AND OBJECTIVE: Gastric cancer is a heterogeneous and complicated epithelial cancers. Chronic H. pylori and EBV infection, as well as intestinal microbiota exposure make gastric cancer encountered a complex tumor immune microenvironment. Mitophagy and m6A are deeply involved in immune microenvironment in the development of tumors. METHODS: We used integrating machine learning of bulk and single cell RNA sequencing to explore the immune mechanisms of m6A related mitophagy genes (MRMGs) in gastric cancer. RT-qPCR and immunochemistry were used to verify gene expression. RESULTS: Prognostic model that involves a total of 20 DE-MRMGs exhibited a performance property in prognosis, immunotherapy prediction and tumor mutation burden in patients with gastric cancer. And significant difference between high-risk group and low-risk group focus on T cells which clarified in both bulk RNA and single cell RNA data. In terms of mechanism, vimentin may participates in T cell differentiation of malignant gastric cancer. Meanwhile, vimentin expression in patients display a significant increasing in low differentiated gastric cancer than high differentiated gastric cancer. CONCLUSIONS: Vimentin may be a diagnostic marker to draw the distinction between low and high differentiated gastric cancer in the mechanism of probably affecting T cell differentiation.
Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage.
Liu X, He Y, Wang Z … +2 more , Huang M, Liu X
Comput Methods Programs Biomed · 2026 May · PMID 41793967 · Publisher ↗
BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits considerable heterogeneity, presenting substantial challenges for clinicia...
BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits considerable heterogeneity, presenting substantial challenges for clinicians in establishing realistic goals. While integrative AI paradigms have shown success in other complex medical domains, existing models often rely on single-modality data, limiting their predictive accuracy and clinical utility. This study aims to develop and validate a multimodal predictive model integrating CT imaging, clinical data, and rehabilitation assessments to simultaneously predict motor recovery and global rehabilitation outcomes following cerebral hemorrhage. METHODS: We conducted a retrospective study involving 739 patients (315 for motor function prediction and 424 for rehabilitation outcome assessment) who received rehabilitation therapy after cerebral hemorrhage. To predict motor function, we constructed a late-fusion deep learning model leveraging 3D-DenseNet for CT neuroimaging and Multi-Layer Perceptron (MLP) for clinical and laboratory features. To predict rehabilitation outcomes, a Gradient Boosting Decision Tree (GBDT) model was developed and validated using 5-fold and 10-fold cross-validation, comparing it against other machine learning algorithms, including SVR, Random Forest and AdaBoost. Model performance was assessed using metrics including AUC and R². Additionally, univariate and multivariate regression analysis were performed to identify significant factors influencing motor recovery and rehabilitation outcomes. RESULTS: A total of 739 patients were included. The multimodal fusion model achieved an AUC of 0.856 (95 % CI: 0.741-0.971) and an F1 score of 0.897 (95 % CI: 0.819-0.975), significantly outperforming the imaging-only (AUC: 0.833) and clinical-only (AUC: 0.749) models. For rehabilitation outcome prediction, the GBDT model achieved an R of 0.849 (95 % CI: 0.803-0.887), demonstrating superior stability and accuracy over other models. Additionally, multivariate analysis revealed that serum albumin (ALB), neutrophil percentage (NEUT%), triglycerides (TG), and thrombin time (TT) were independent predictors of motor recovery, while age, admission mBI, and time to start rehabilitation significantly influenced functional outcomes. CONCLUSION: This study confirms that a multimodal deep learning framework integrating routinely available CT imaging and clinical biomarkers provides high predictive value for simultaneously forecasting motor recovery and global functional outcomes after ICH. This proof-of-concept approach offers a reproducible, data-driven tool for early risk stratification, facilitating the formulation of individualized rehabilitation strategies and optimizing resource allocation in clinical workflows.
Assessment of socioeconomic and demographic risk factors for low birth weight using model-agnostic explainable ensembles.
Hamja MA, Hasan M, Jahan M … +1 more , Hassan MZ
Comput Methods Programs Biomed · 2026 May · PMID 41785538 · Publisher ↗
BACKGROUND AND OBJECTIVE: Low birth weight (LBW) is a major global public health concern, strongly linked to neonatal morbidity and long-term health complications. Early prediction of LBW is essential to reduce neonatal...
BACKGROUND AND OBJECTIVE: Low birth weight (LBW) is a major global public health concern, strongly linked to neonatal morbidity and long-term health complications. Early prediction of LBW is essential to reduce neonatal mortality and guide targeted healthcare interventions. This study proposes a predictive framework integrating machine learning (ML), deep learning (DL), and model-agnostic eXplainable Artificial Intelligence (XAI) to identify key socioeconomic and demographic determinants of LBW. METHODS: Data from the Bangladesh Demographic and Health Survey (BDHS), comprising 1574 participants and 12 variables, are analyzed. Key predictors included maternal age, education, household wealth, geographic region, birth order, and maternal BMI. Chi-square tests assess variable associations. A stacking ensemble model, SmartFusion-LR5, is developed, combining K-Nearest Neighbors, Logistic Regression (LR), Decision Tree, Random Forest, and Naive Bayes, with LR as the meta-learner. Model performance is evaluated using accuracy, precision, recall, area under the curve (AUC), F1-score, and Matthews correlation coefficient (MCC). RESULTS: Significant disparities in LBW prevalence are observed across geographic divisions, with higher parental education and socioeconomic status associated with healthier outcomes. The SmartFusion-LR5 model achieves the highest overall discriminative capability compared to baselines, attaining 93.0% accuracy, 86.7% precision, 99.8% recall, 92.8% F1-score, 94.0% AUC, and an MCC of 86.0%. Comparable performance also obtained from SmartFusion-XGB4 (91.8% accuracy, 86.2% precision, 99.6% recall, 92.4% F1-score, 92.2% AUC, MCC 84.8%) and SmartFusion-RF4 (91.7% accuracy, 86.2% precision, 99.2% recall, 92.2% F1-score, 92.0% AUC, MCC 84.4%). Global XAI methods identified age at first birth, division, residence, wealth, and husband's education as key determinants, while local explanations revealed individual feature impacts. CONCLUSIONS: The proposed framework offers a robust, interpretable, and scalable approach for early LBW risk prediction, supporting targeted maternal and child health interventions in resource-constrained settings.
Explainable machine learning framework for the molecular classification of triple negative breast cancer.
L BC, Patel T, Charan D … +9 more , Sinha MG, S R, Jain M, Bhardwaj A, Gupta A, J D, K A, Mishra A, Mishra D
Comput Methods Programs Biomed · 2026 May · PMID 41775141 · Publisher ↗
BACKGROUND AND OBJECTIVE: The difference in molecular characteristics of Triple negative breast cancer (TNBC) aids in distinguishing between its four prominent subtypes- basal-like 1, basal-like 2, mesenchymal, and lumin...
BACKGROUND AND OBJECTIVE: The difference in molecular characteristics of Triple negative breast cancer (TNBC) aids in distinguishing between its four prominent subtypes- basal-like 1, basal-like 2, mesenchymal, and luminal androgen receptor. This study presents the first integrative framework that combines explainable AI with machine learning approaches to classify TNBC subtypes. Unlike conventional models, our approach offers interpretability while enabling biomarker prioritization by identifying key hub genes that drive subtype-specific predictions. METHODS: In the experiment 783 cases (BL1 (160), BL2 (75), M (151), LAR (106), non-TNBC (291) reported in Gene Expression Omnibus (GEO) and Genomic Data Commons (GDC) data portal were used for the analysis. The proposed framework comprises modules for the identification of gene signatures for the four-subtype followed by the classification model based on eight different machine learning algorithms. Random Forest classifier was found to be best model with 96 % testing accuracy, which was elected for Explainable framework using Shapley Additive Explanations. RESULTS: Explainable biomarker module could provide a set of 47 biomarkers which is relevant in distinguishing the four types on triple negative breast cancer. The biomarkers could have the potential to be considered for TNBC prognosis in clinical setting. CONCLUSION: Key findings highlight the hub genes CDC20, CDCA2, PIMREG, KIF2C, and CENPW, implicating pathways such as ubiquitin-proteasome signaling and microtubule dynamics. These insights pave the way for biomarker-driven therapies and precision medicine in triple negative breast cancer.
Continuous vs pulsatile arterial cannula flow for venoarterial extracorporeal membrane oxygenation: A multiscale computational fluid dynamics analysis.
Wickramarachchi A, Neidlin M, Khamooshi M … +4 more , Benitez J, Wu EL, Fraser JF, Gregory SD
Comput Methods Programs Biomed · 2026 May · PMID 41771197 · Publisher ↗
BACKGROUND AND OBJECTIVE: Venoarterial extracorporeal membrane oxygenation (VA ECMO) circuits typically utilise a continuous flow (CF) of blood to support patients suffering from refractory cardiorespiratory dysfunction....
BACKGROUND AND OBJECTIVE: Venoarterial extracorporeal membrane oxygenation (VA ECMO) circuits typically utilise a continuous flow (CF) of blood to support patients suffering from refractory cardiorespiratory dysfunction. Pulsatile flow (PF) VA ECMO is an emerging technology being developed to overcome adverse effects associated with non-physiological CF VA ECMO such as worsening of microcirculatory and cardiac function. However, the flow dynamics associated with PF VA ECMO, such as positioning of the watershed region, wall shear stress, and ventricular unloading are still largely unknown. Therefore, to address this gap, our study aimed to utilise computational fluid dynamics (CFD) to compare the arterial cannula flow characteristics generated by CF and PF VA ECMO. METHODS: A multiscale CFD model was created using a patient-specific aortic geometry and employed a closed-loop lumped parameter network as boundary conditions. Mean VA ECMO flow rates of 3, 4, and 5 L/min were simulated for both CF and counter-pulsed PF scenarios. RESULTS: The hemodynamic results demonstrated increased stroke volume, ejection fraction, and coronary flow during PF VA ECMO, and decreased left ventricular volumes, afterload, and pressure-volume areas, when compared to CF VA ECMO. Delivery of oxygen saturated blood from VA ECMO to the upper body decreased slightly during PF VA ECMO during 4 L/min of support. Lastly, wall shear stress on the aortic wall increased substantially during PF VA ECMO, when compared to CF VA ECMO. CONCLUSIONS: The findings from this study suggest varied hemodynamic and flow dynamic outcomes when comparing CF and PF VA ECMO, each with their own benefits and drawbacks.
Reliable detection of focal onset impaired awareness seizures in patients with epilepsy using wearable ECG: Development and validation study.
Alhaskir M, Kutafina E, Linke F … +16 more , Fischer FP, Schriewer E, Lauxmann S, Klett K, Hofmeister J, Lutz F, Burow L, Cicanic M, Khosrawikatoli S, Wolking S, Mayer T, Beniczky S, Kegele J, Röhrig R, Koch H, Weber Y
Comput Methods Programs Biomed · 2026 May · PMID 41764784 · Publisher ↗
BACKGROUND: Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and condition management in patients with epilepsy. Traditional reliance...
BACKGROUND: Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and condition management in patients with epilepsy. Traditional reliance on patient self-reporting can lead to inaccuracies, hindering effective treatment. Wearable-based seizure detection algorithms offer a promising solution, however, developing an efficient method for detecting FIAS remains a challenge. Additionally, as data quality can vary in wearable settings, the absence of continuous data quality assessment poses a concern for the reliability of such algorithms. OBJECTIVE: The objective of our study is to develop and evaluate the performance and feasibility of FIAS detection algorithm with automatic data quality assessment (ADQA) using a wearable electrocardiography (ECG) device. We will also conduct an exploratory analysis of inter-individual variability in autonomic seizure signatures to identify potential future candidates, or "responders" to this system. Performance will be evaluated using sensitivity, false alarm rate per 24 h (FAR/24), positive predictive value, and F1-Score. METHODS: A multicenter study was conducted across three epilepsy centers and recruited patients of all ages who were admitted to video-EEG monitoring for a minimum of 24 h consecutively. Data were collected using a wearable ECG device. The algorithm involved R-peak detection to identify heartbeats, extraction of knowledge domain heart rate variability features, ADQA, heart rate (HR) filter to address class imbalance, and a deep learning model for the final detection step. The algorithm was validated in a leave-one-patient-out (LOPO) approach using expert-labeled ictal events from video-EEG monitoring as ground truth. RESULTS: A total of 236 patients were recruited, of whom 49 patients experienced at least one FIAS, resulting in 3278 h of ECG data and 260 seizures. Two patients with 33 seizures were excluded due to a technical error in the recording files, leaving 47 patients for analysis. After data quality screening, 161 seizures from 38 patients met the quality criteria. In this group, the median sensitivity was 66.6% (95% CI:33.3%-100%) with a median FAR/24 of 5.2 (95% CI:3.5-8.2). An exploratory responder analysis identified 20 patients with a detection sensitivity of ≥66.6%, for whom the median sensitivity was 100% (95% CI: 92%-100%) and the median FAR/24 was 4.3 (95% CI: 3-7). Finally, removing ADQA from the test data reduced the algorithm's reliability, while removing it from training and test data reduced sensitivity, robustness, and reliability. CONCLUSIONS: The proposed algorithm demonstrated reasonable performance in patients whose wearable ECG data met the ADQA quality criteria (n = 38), with the highest detection performance observed in an exploratory responder subgroup (n = 20). These findings highlight the potential of ECG-based wearable systems for improving FIAS monitoring and underscore the importance of data quality in ensuring reliable algorithm performance. TRIAL REGISTRATION: German Clinical Trials Register: DRKS00026939.
A multimodal data-based model for breast cancer diagnosis.
Wang H, Wei L, Li J … +3 more , Liu B, Fang J, Mooney C
Comput Methods Programs Biomed · 2026 May · PMID 41759488 · Publisher ↗
BACKGROUND AND OBJECTIVE: Developing multimodal data-driven diagnostic systems has become a key clinical strategy for improving breast cancer outcomes. However, effectively modeling multimodal features remains challengin...
BACKGROUND AND OBJECTIVE: Developing multimodal data-driven diagnostic systems has become a key clinical strategy for improving breast cancer outcomes. However, effectively modeling multimodal features remains challenging due to substantial semantic heterogeneity, scale discrepancies, and the inherent difficulty of cross-modal alignment. Although existing studies have proposed various multimodal fusion methods, most rely on direct feature concatenation or shallow integration, which fail to capture fine-grained intra-modality semantics as well as the complex interactions between histopathological and genomic modalities. METHODS: In this study, we propose a multimodal diagnostic framework based on Feature Enhancement and Semantic Collaborative Alignment (FESCA). The method incorporates a semantic-guided modality feature enhancement mechanism that effectively extracts and strengthens diagnostic cues from both pathological images and genomic data. In addition, a contrastive-learning-based cross-modal alignment strategy is introduced to map heterogeneous modalities into a unified semantic space and achieve deep semantic collaboration through contrastive optimization. To ensure robust breast cancer classification under varying modality availability, a multimodal collaborative diagnostic strategy is employed to dynamically adapt the feature representations. RESULTS: We evaluate FESCA on the TCGA-BRCA dataset, and the experimental results demonstrate that it outperforms state-of-the-art methods in breast cancer classification while significantly improving both intra-modality representation quality and cross-modal semantic alignment. CONCLUSION: To enhance accessibility and practical application, we developed a web-based breast cancer pathological staging diagnosis system to visualize and deploy the FESCA model, demonstrating a step toward clinical application and providing a benchmark for other research methods.
Optimal design for multi-compartment cell elasticity estimation using AFM and super-resolution imaging.
Mendiola EA, Walther BK, Mojiri A … +4 more , Cooke JP, Ohayon J, Pettigrew RI, Avazmohammadi R
Comput Methods Programs Biomed · 2026 May · PMID 41759487 · Full text
BACKGROUND AND OBJECTIVE: The transduction of external forces to the internal components of the cell has critical implications for cell behavior in health and disease. Despite advances in techniques such as atomic force...
BACKGROUND AND OBJECTIVE: The transduction of external forces to the internal components of the cell has critical implications for cell behavior in health and disease. Despite advances in techniques such as atomic force microscopy (AFM), accurately and reproducibly estimating the mechanical properties of multiple individual subcellular compartments remains challenging. This is often due to variability in measurements, the lack of a standard "inverse" approach to estimate unknown properties, and an often ill-posed inverse problem. This study presents an integrated experimental-computational framework for the optimal design of an inverse approach to estimate multi-compartment cell properties, focusing on the behavior of the nuclear membrane, cytoplasm, and nucleoplasm. The optimal design approach identifies an ideal set of AFM measurements that minimizes the dependence of the estimated multi-compartment properties on AFM probing locations, thereby improving the reproducibility of mechanical property estimations. METHODS: Super-resolution imaging was used to construct a 3-D computational model of a human umbilical vein endothelial cell (HUVEC). An inverse modeling approach was then used to fit experimental data to a hyperelastic constitutive model, accounting for large-deformation nonlinearities. Simulations incorporating visco-hyperelasticity were conducted to explore viscous effects. RESULTS: Our approach leads to quantification of the mechanical properties of the nucleoplasm, nuclear membrane, and cytoplasm with minimal dependence on loading conditions. CONCLUSIONS: We expect our approach to assist with standardizing the biomechanical characterizations of subcellular structures, improve the consistency and reproducibility of the estimations across mechanobiological studies, and ultimately improve our understanding of the role of mechanotransduction in disease progression.
An automated computational pipeline for generating large-scale cohorts of patient-specific ventricular models in electromechanical in silico trials.
Doste R, Camps J, Wang ZJ … +8 more , Berg LA, Holmes M, Smith H, Beetz M, Li L, Banerjee A, Grau V, Rodriguez B
Comput Methods Programs Biomed · 2026 May · PMID 41759486 · Publisher ↗
BACKGROUND AND OBJECTIVE: In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In silico trials no...
BACKGROUND AND OBJECTIVE: In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In silico trials not only minimise patient risks but also reduce reliance on animal testing. However, implementing in silico trials presents several time-consuming challenges. It requires the creation of large cohorts of virtual patients. Each virtual patient is described by their anatomy with a volumetric mesh and electrophysiological and mechanical dynamics through mathematical equations and parameters. Furthermore, simulated conditions need definition including stimulation protocols and therapy evaluation. For large virtual cohorts, this requires automatic and efficient pipelines for the generation of corresponding files. In this work, we present a computational pipeline to automatically create large virtual patient cohort files to conduct large-scale in silico trials through cardiac electromechanical simulations. METHODS: The pipeline automatically generates anatomical labels, volumetric meshes suited for electromechanical simulations, and all necessary fields and files for the simulations, including stimulation information, from unprocessed surface meshes and input parameters, without requiring training data. It also handles patient heterogeneity and supports integration with algorithms for Purkinje network generation and electrocardiogram personalisation. RESULTS: The pipeline was applied across several datasets to generate over 100 virtual patients. Simulations were performed to demonstrate its capacity to conduct in silico trials for virtual patients using verified and validated electrophysiology and electromechanics models for the context of use. The proposed pipeline demonstrated its adaptability to accommodate different types of ventricular geometries and mesh processing tools, ensuring its versatility in handling diverse clinical datasets. CONCLUSIONS: By establishing an automated framework for large scale simulation studies as required for in silico trials and providing open-source code, our work aims to support scalable, personalised cardiac simulations in research and clinical applications.
A Multi-physics model of flow from coronary angiography: Insights to microvascular function.
Yang H, Zhang J, Assi IZ … +3 more , Nallamothu BK, Garikipati K, Figueroa CA
Comput Methods Programs Biomed · 2026 May · PMID 41759485 · Publisher ↗
BACKGROUND AND OBJECTIVE: Coronary Microvascular Dysfunction (CMD) is characterized by impaired vasodilation and can lead to insufficient blood flow to the myocardium during stress or exertion, affecting millions of peop...
BACKGROUND AND OBJECTIVE: Coronary Microvascular Dysfunction (CMD) is characterized by impaired vasodilation and can lead to insufficient blood flow to the myocardium during stress or exertion, affecting millions of people globally. Although invasive wire-based diagnostics such as the index of microcirculatory resistance (IMR) and coronary flow reserve (CFR) provide valuable insights, their adoption in clinical settings remains limited due to procedural complexity and inconsistency. Coronary angiography, one of the most commonly used imaging modalities, offers valuable flow information that assists in diagnosing CMD. However, this information is not fully understood or utilized in current clinical practice. METHODS: In this study, a 3D-0D coupled multi-physics computational fluid dynamics (CFD) model was developed and calibrated to simulate and study the process of contrast injection and washout during clinical angiography. A contrast intensity profile (CIP) was introduced to describe the dynamics of coronary angiography data. Additionally, sensitivity studies were conducted to evaluate the influence of various coronary lumped parameter model (LPM) parameters on the shapes of CIPs. RESULTS: The multi-physics model can be effectively calibrated to produce physiologically meaningful hemodynamic results. Sensitivity studies reveal that resistance has a greater impact on the rising and falling slopes of CIP than capacitance, with higher resistance amplifying this effect. CONCLUSION: This study presents a promising modeling framework for interpreting angiographic data and ultimately extracting information concerning coronary microcirculation. While promising, the approach has not yet been validated in vivo, highlighting the need for future clinical studies.
Hemodynamic characteristics of type I Endoleak and intra-prosthetic thrombus following endovascular aneurysm repair: A computational fluid dynamics study.
Wen J, Yuan F, Li W … +4 more , Huang Q, Li H, Feng H, Liu M
Comput Methods Programs Biomed · 2026 May · PMID 41740542 · Publisher ↗
BACKGROUND: Endovascular aneurysm repair (EVAR) is preferred for abdominal aortic aneurysms (AAAs) due to minimal invasiveness, but complications like Type I endoleaks (T1EL) and intra-prosthetic thrombus (IPT) persist....
BACKGROUND: Endovascular aneurysm repair (EVAR) is preferred for abdominal aortic aneurysms (AAAs) due to minimal invasiveness, but complications like Type I endoleaks (T1EL) and intra-prosthetic thrombus (IPT) persist. While current risk prediction routinely involves anatomical assessment, the specific postoperative hemodynamic environments associated with these distinct complications remain under-investigated. METHODS: This study integrates patient-specific computed tomography (CT) angiography with a computational fluid dynamics (CFD)-based thrombus growth model to analyze postoperative geometries in 12 patients divided into three groups: T1EL, IPT, and no complications (Control). Key hemodynamic descriptors included flow patterns, wall shear stress (WSS), helicity, pressure gradients, and predicted thrombus distribution. RESULTS: T1EL patients showed complex flow patterns and distinct pressure gradients near the proximal or distal sealing zones. The IPT group exhibited significantly low time-averaged wall shear stress (TAWSS) and high relative residence time (RRT) within the graft limbs, fostering platelet aggregation. CFD simulations indicated different spatial distributions of potential thrombus formation between groups. Specifically, T1EL cases demonstrated stronger 3D helical flow features, while IPT cases had weaker rotational flow. Notably, graft geometry analysis revealed that cross-limb configurations induced specific flow disturbances distinct from standard configurations. CONCLUSIONS: By combining patient-specific biomechanics and thrombus modeling, this study offers new insights into the distinct flow phenotypes of post-EVAR complications. It highlights hemodynamic descriptors-helicity, TAWSS, oscillatory shear index (OSI), RRT, and endothelial cell activation potential (ECAP)-as potential quantitative metrics to complement anatomical assessment. Findings also suggest cross-limb graft configurations may modulate flow dynamics, potentially increasing thrombotic risk, providing preliminary clinical guidance requiring validation.
Prompt-to-policy: Leveraging large language models to guide deep reinforcement learning in public health emergencies.
Yang H, Zhang C, Yang F … +3 more , Zhang X, Zhao Y, Sun H
Comput Methods Programs Biomed · 2026 May · PMID 41734496 · Publisher ↗
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncertain conditions. Traditional optimization methods often struggle with co...
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncertain conditions. Traditional optimization methods often struggle with computational complexity and real-time, heterogeneous data. To address these challenges, we propose a hybrid intelligent agent combining an enhanced Double Deep Q-Network (D2QN-JDA) with large language models (LLMs). The D2QN-JDA improves learning stability and adaptability through joint state-action inputs, dynamic exploration rate, and adaptive reward normalization. The LLM component uses Retrieval-Augmented Generation (RAG) to integrate structured and unstructured data for real-time decision support. Experiments based on data from the Hong Kong COVID-19 outbreak show that the D2QN-JDA outperforms dynamic programming, greedy algorithms, genetic algorithms, and Q-learning, achieving reductions in cost. The LLM component also outperforms manual and regex methods in both single- and multi-point data extraction, enhancing accuracy, recall, F1 score, cost, and time. Our framework effectively addresses complex, multi-objective resource allocation in public health crises.
CardioSynth: Parameter-driven cardiac MRI generation via oriented bounding boxes.
Banerjee S, Mazumder O, Sinha A
Comput Methods Programs Biomed · 2026 May · PMID 41734495 · Publisher ↗
BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging provides detailed anatomical information but is costly and not feasible for routine monitoring. Accurate control of cardiac substructure areas is essential for...
BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging provides detailed anatomical information but is costly and not feasible for routine monitoring. Accurate control of cardiac substructure areas is essential for studying development, adaptation, and disease progression. This work introduces a framework for synthetic cardiac imaging that enables parameter-driven area modifications using oriented bounding box representations while preserving anatomical plausibility. METHODS: We propose a three-stage framework that integrates: (1) Encoding each cardiac substructure as an oriented bounding box to enable structured representation and easier shape manipulation. (2) A progressive label modification algorithm to apply parameter-driven area changes while maintaining anatomical consistency. (3) A bounding-box-to-segmentation model for reconstructing detailed masks and (4) A diffusion-based segmentation-to-image synthesis model for generating realistic cardiac magnetic resonance images. The oriented bounding box encoding serves as the foundation for controlled anatomical transformations, while the subsequent models ensure structural plausibility and image fidelity. RESULTS: Experiments show that oriented bounding box encoding enables more accurate control of cardiac substructure area modifications than conventional approaches. Both increments and decrements exhibit a systematic deviation of about 5%, which is effectively corrected by applying a 5% calibrated input, reducing mean errors to within ±1%. Generated images remain anatomically plausible and structurally consistent. CONCLUSIONS: The proposed framework enables parameter-driven cardiac MRI synthesis with precise substructure area control. By combining oriented bounding box encoding, progressive modification, and diffusion modeling, it achieves anatomically consistent results while reducing reliance on repeated scans, supporting applications in longitudinal monitoring and progression studies.
Emboli transport in a full patient-specific aortic tree: Assessment of abdominal organ injury risk during cardiopulmonary bypass.
Arefin NM, Good BC
Comput Methods Programs Biomed · 2026 May · PMID 41734494 · Publisher ↗
BACKGROUND AND OBJECTIVE: Cardiopulmonary bypass (CPB), though indispensable in cardiac surgery, carries significant risks of systemic embolization and organ injury. While cerebral and cardiac complications have been tho...
BACKGROUND AND OBJECTIVE: Cardiopulmonary bypass (CPB), though indispensable in cardiac surgery, carries significant risks of systemic embolization and organ injury. While cerebral and cardiac complications have been thoroughly investigated, the impact of emboli on abdominal organs remains largely unexplored. This study aims to identify hemodynamic and embolus-related factors governing embolic transport to key abdominal arteries during CPB, using computational modeling in a patient-specific aortic anatomy. METHODS: A validated OpenFOAM-based computational fluid dynamics (CFD) framework was integrated with Lagrangian particle tracking (LPT) and applied under steady, pump-driven flow conditions representative of CPB to simulate embolic trajectories within a patient-specific aorta. Parametric analyses were conducted to evaluate the individual effects of blood flow rate (3-5 LPM), hemodiluted blood viscosity (1.5-3.5 cP), and embolus size (0.5-2.5 mm) on embolic distribution across major abdominal aortic branches including the renal, hepatic, splenic, mesenteric, and iliac arteries. RESULTS: Lower blood viscosity (1.5 cP) and higher CPB flow rate (5 LPM) significantly influenced embolic transport, both independently and in combination. Under combined conditions, emboli transport to the renal and hepatic arteries increased from 17% to 27% and from 7.1% to 10.7%, respectively. Reduced viscosity alone produced the greatest rise, with increases of 18% and 35% in the renal and hepatic arteries. Increasing CPB flow rate from 3 to 5 LPM also elevated emboli exit across all branches, with renal transport rising by 29%. Also, larger emboli (2.5 mm) exhibited higher escape rates of 14% and 26% into the renal and hepatic arteries, respectively, compared to smaller emboli. These tendencies are consistent with trends reported in clinical studies of post-CPB complications. CONCLUSIONS: This study presents the first CFD-based analysis of embolic transport to the abdominal organs during CPB, revealing critical pathways previously overlooked in both clinical and computational research. The results demonstrate that lower blood viscosity, higher CPB flow rates, and larger emboli significantly increase embolic dispersion into abdominal arteries. To mitigate these potential risks, this study highlights the need for optimized CPB perfusion strategies to minimize embolic burden and improve intraoperative protection of abdominal organs during cardiac surgery.
Towards lightweight stress monitoring on biometric data for IoMT environments.
Montoya-Peña C, Gaitán-Guerrero JF, Espinilla M … +1 more , López JL
Comput Methods Programs Biomed · 2026 May · PMID 41719782 · Publisher ↗
BACKGROUND AND OBJECTIVE: Stress is a physiological response mechanism that enables humans to react to perceived threats through a fight-or-flight response. While beneficial in acute situations, prolonged exposure to str...
BACKGROUND AND OBJECTIVE: Stress is a physiological response mechanism that enables humans to react to perceived threats through a fight-or-flight response. While beneficial in acute situations, prolonged exposure to stress can lead to significant physical and mental health issues, making early and reliable detection essential. Although many existing approaches achieve high accuracy by relying on numerous physiological signals and features, such solutions are often unsuitable for Internet of Medical Things (IoMT) applications that increasingly rely on edge computing paradigms. In these scenarios, stress detection models must operate directly on resource-constrained devices with limited computational and energy budgets. Therefore, this work proposes a lightweight and efficient methodological framework for stress detection, specifically designed for edge-based IoMT deployment. METHODS: Eight supervised Machine Learning (ML) algorithms were evaluated: Random Forest (RF), LightGBM, CatBoost, XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and a Multilayer Perceptron (MLP). All models were trained using Heart Rate Variability (HRV) and respiratory features extracted from the WESAD dataset. The proposed framework combines population-level training with subject-specific adaptation and evaluates model performance under progressive dimensionality reduction using subsets of 15, 10, 8, 6, and 4 features. RESULTS: The proposed two-stage framework demonstrates that subject-specific adaptation significantly improves stress detection performance. XGBoost achieved the highest balanced accuracy (95.1% ± 4.7%) using 10 features, outperforming the configuration with all 15 variables. Crucially, the study identifies a reduced set of 6 features as the optimal deployment configuration; despite its further reduced feature set, it showed no statistically significant performance loss compared to the 10-feature model (95% CI: -0.0078, 0.0068) and maintained a 99.6% probability of outperforming the best models from all other architectures evaluated. CONCLUSIONS: The results show that accurate and personalized stress detection is feasible using reduced feature sets, enabling efficient, interpretable, and real-time deployment of ML models in wearable and IoMT-based monitoring systems.
A novel indirect method for deriving reference intervals through iterative data cleaning guided by self-organizing maps of multi-test patterns (SOM-clean).
Ichihara K, Yamashita T, Borai A
Comput Methods Programs Biomed · 2026 May · PMID 41719685 · Publisher ↗
BACKGROUND & OBJECTIVE: Most existing methods for indirectly deriving reference intervals (RIs) from routine laboratory databases use univariate approaches with limited or no rigorous data cleaning. Recognizing the poten...
BACKGROUND & OBJECTIVE: Most existing methods for indirectly deriving reference intervals (RIs) from routine laboratory databases use univariate approaches with limited or no rigorous data cleaning. Recognizing the potential of multivariate data-mining strategies, we developed novel software-SOM-clean-that employs self-organizing map (SOM) clustering for iterative exclusion of records exhibiting atypical multi-test patterns. METHODS: We retrieved records for 22 major health-screening tests (HSTs) from a Saudi Arabian laboratory participating in a RI study. After excluding records from frequently tested individuals and those with <10 HST results, 37,285 records remained for analysis. Initial crude RIs were calculated parametrically using a two-parameter Box-Cox power transformation. All transformed values were standardized against these RIs to generate uniform-scale values, so that any result within RI limits fell between ±1.96. The self-organizing map (m × m cells, m = 5-8) was initialized with normal random values, and records were clustered into cells with highest similarity. Cells' patterns were updated by records assigned to each of them. This learning process of the map was repeated until equilibrium. Subsequently, cells exhibiting atypical features were excluded, and RIs were recalculated using records from the remaining cells. This process was repeated iteratively until all RIs stabilized. RESULTS: Histograms of retrieved results frequently exhibited peaks differing in shape and location from those in the direct study (n = 880). The goodness-of-fit (GOF) of SOM-clean RIs was assessed by skewness, kurtosis, and Kolmogorov-Smirnov test P-values after transformation, as well as by the bias ratio of reference limits compared with the direct study. GOF depended on map size and criteria for identifying atypical cells; the software therefore incorporated an all-inclusive search for optimal conditions referencing the direct study RIs. By using the optimal settings, SOM-clean achieved excellent GOF of RIs simultaneously across nearly all HSTs, indicating conformity of the estimated RIs to the healthy status. In comparison, RIs derived using a representative indirect method (refineR) were generally broader or biased, particularly for tests with highly skewed distributions. CONCLUSION: SOM-clean represents a practical and robust parametric tool for estimating RIs indirectly from routine laboratory data employing a novel multivariate-based data cleaning scheme.
Computational hemodynamic analysis of idealized coronary arteries with cylindrical and conical stents.
Telöken KN, Ghisleni ID, Zinani FSF … +1 more , Wermuth DP
Comput Methods Programs Biomed · 2026 May · PMID 41702231 · Publisher ↗
BACKGROUND AND OBJECTIVE: In Brazil, coronary angioplasty with stent implantation is a primary intervention for cardiovascular diseases, yet in-stent restenosis remains a significant complication. Recent proposals sugges...
BACKGROUND AND OBJECTIVE: In Brazil, coronary angioplasty with stent implantation is a primary intervention for cardiovascular diseases, yet in-stent restenosis remains a significant complication. Recent proposals suggest transitioning from traditional cylindrical stents to conical geometries to better align with vascular physiology. This study aims to compare the performance of cylindrical and conical stents and investigate the influence of varying strut thicknesses on hemodynamic parameters. METHODS: The study employed computational modeling using both Fluid-Structure Interaction (FSI) and Computational Fluid Dynamics (CFD) simulations to quantify hemodynamic parameters including Time-Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT). A total of 12 simulations were performed (6 FSI and 6 CFD) on models of cylindrical and conical arteries with stent strut thicknesses ranging from 0.1 mm to 0.3 mm. The finite volume method was used for the fluid domain, while the finite element method was applied to the solid domain (arterial wall and stent). Blood was modeled as a non-Newtonian fluid using the Carreau model, with Reynolds numbers from 251 to 381 and Womersley numbers from 2.23 to 3.78. RESULTS: Quantitative analysis revealed that rigid-wall CFD consistently underestimates the risk of restenosis compared to FSI. Specifically, FSI predicted areas of critical Time-Averaged Wall Shear Stress (TAWSS ≤ 1 Pa) that were 12% to 46% larger than those predicted by CFD. Strut thickness emerged as a dominant factor; increasing thickness to 0.3 mm resulted in WSS values approximately three times lower than the 0.1 mm models, significantly expanding recirculation zones. Regarding geometry, while cylindrical stents exhibited concentrated high Oscillatory Shear Index (OSI) at the distal edge, conical stents demonstrated a more distributed OSI pattern and a markedly improved Relative Residence Time profile, reducing peak RRT at the distal edge by approximately 60% compared to cylindrical models (12.25Pa vs. 29.94Pa), thereby mitigating stagnation and potential edge restenosis. CONCLUSIONS: The findings confirm that neglecting arterial compliance (CFD only) leads to a substantial underestimation of hemodynamic risk. Both stent geometry and strut thickness are critical; while conical stents offer better risk distribution, thicker struts can negate these benefits. Optimizing these parameters is essential for next-generation stent designs.
Identifying neurotrophic factor related genes at the crosstalk between glioblastoma and ischemic stroke.
Shi C, Ding L, Wang D … +4 more , Zhu J, Zheng X, Zhang L, Zhou Y
Comput Methods Programs Biomed · 2026 May · PMID 41691877 · Publisher ↗
BACKGROUND: Glioblastoma multiforme (GBM) and ischemic stroke (IS) are two major neurological disorders contributing substantially to global mortality and disability. GBM elevates IS risk via prothrombotic mechanisms, wh...
BACKGROUND: Glioblastoma multiforme (GBM) and ischemic stroke (IS) are two major neurological disorders contributing substantially to global mortality and disability. GBM elevates IS risk via prothrombotic mechanisms, while IS may accelerate glioma progression through ischemia-driven neuroinflammation. Identifying shared molecular mediators is essential for understanding their bidirectional pathophysiology. METHODS: A systems biology approach was implemented to investigate shared neurotrophic factor-related genes (NFRGs) between GBM and IS. A total of 2871 NFRGs were screened from Genecards, with Caspase-3 (CASP3) and Protein Arginine N-Methyltransferase 6 (PRMT6) identified as core regulators. Multi-omics validation included: 1) Differential expression profiling across The Cancer Genome Atlas (TCGA)-GBM and Gene Expression Omnibus (GEO) stroke datasets; 2) Prognostic stratification using Kaplan-Meier (KM) survival curves with log-rank test and Cox proportional hazards regression; 3) Immune microenvironment analysis via CIBERSORT; 4) Experimental validation in middle cerebral artery occlusion (MCAO) mice and GBM cell lines (U87MG, T98G, A172) using Real-Time Quantitative Reverse Transcription PCR (qRT-PCR), Western blot (WB), and immunofluorescence (IF). RESULTS: CASP3 and PRMT6 were significantly upregulated in both GBM and IS (P < 0.05). KM survival analysis with log-rank test showed that high expression of CASP3 and PRMT6 was strongly associated with poorer overall survival (OS) in GBM patients (P < 0.001; Hazard Ratio (HR) = 4.375, 95% Confidence Interval (CI) = 3.336-5.738 for CASP3; HR = 4.547, 95% CI = 3.429-6.029 for PRMT6). Receiver operating characteristic (ROC) analysis confirmed robust diagnostic (Area Under the Curve (AUC) > 0.7) and prognostic efficacy for both markers. IF validated their elevated expression in ischemic brain tissues of Middle Cerebral Artery Occlusion (MCAO) mice, while qRT-PCR and WB confirmed higher expression in GBM cells versus normal glial cells. Immune infiltration analysis indicated that CASP3 and PRMT6 are associated with immunosuppressive remodeling in GBM, suggesting their role as a molecular bridge between the two diseases. CONCLUSIONS: Our findings identify CASP3 and PRMT6 as dual molecular mediators coordinating GBM progression and post-IS pathological processes. Targeting these genes may provide novel therapeutic avenues for preventing GBM-associated IS and improving neuro-oncological outcomes.
Harnessing angular geometry in deep learning for protein-ligand binding affinity prediction.
Rahman J, Newton MAH, Mohamed Abdul Cader J … +3 more , Islam MKB, Ali ME, Sattar A
Comput Methods Programs Biomed · 2026 May · PMID 41691876 · Publisher ↗
BACKGROUND: Protein-ligand binding affinity prediction is essential in structure-based drug design, where binding scores guide the selection of promising candidate ligands. Existing deep learning models often use 3D grid...
BACKGROUND: Protein-ligand binding affinity prediction is essential in structure-based drug design, where binding scores guide the selection of promising candidate ligands. Existing deep learning models often use 3D grids, voxelized complexes, or molecular graphs. These representations are resource-intensive and may not capture specific directional interactions. OBJECTIVE: This paper introduces angular geometric features as key descriptors of binding interactions. METHODS: Seven types of dihedral angles between protein and ligand atoms are extracted to encode orientation and geometry. A fully connected ensemble network, called the Angle-Aware Predictor (AAP), integrates these features. RESULTS: On CASF-2016, AAP achieves state-of-the-art results with correlation coefficient (R) of 0.872, root mean squared error (RMSE) of 1.072, mean absolute error (MAE) 0.817, standard deviation (SD) of 1.077, and concordance index (CI) of 0.845. On four additional benchmarks, AAP shows consistent improvements ranging from 0.3% to 36%. CONCLUSION: The angular features are effective, lightweight, and robust descriptors for binding affinity prediction. These results highlight angular geometry as a valuable direction for future structure-based drug discovery. The program and data of AAP are publicly available at https://github.com/juliacse06/AAP.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe