Biomedical Engineering and Computational Biology最新文献

{"title":"BioMem-FWK4α: A Fractional Windkessel-Constant Phase Framework for Analog Simulation and Frequency-Domain Interpretation of Biological Ion Channels.","authors":"Jean Paul Teuguia, Emmanuel Fendzi-Donfack, Wulfran Fendzi Mbasso, Ambe Harrison, Liu Zhe, Zokir Mamadiyarov, Aurélien Kenfack-Jiotsa","doi":"10.1177/11795972261484325","DOIUrl":"10.1177/11795972261484325","url":null,"abstract":"<p><strong>Background: </strong>Classical integer-order membrane and bioelectrical circuit models are useful for idealized ion-channel behavior, but they often fail to reproduce the distributed relaxation, power-law impedance, depressed Nyquist arcs, and memory-dependent phase dispersion observed in biological membranes and tissue interfaces.</p><p><strong>Objectives: </strong>This study aims to develop a fractional four-element Windkessel-constant phase framework, denoted BioMem-FWK4α, for analog simulation and frequency-domain interpretation of biological ion-channel and membrane-like impedance responses.</p><p><strong>Design: </strong>A theoretical, computational, and analog-circuit modeling study was conducted using normalized fractional circuit parameters. The study is intended as a mechanistic modeling framework rather than as a direct experimental validation study.</p><p><strong>Methods: </strong>The classical four-element Windkessel topology was reformulated by replacing ideal compliance with a constant-phase element of order α and by introducing fractional inertial behavior of order β and an active/passive resistance ratio γ. Caputo and Caputo-Fabrizio formulations were compared under explicit zero-initial frequency-domain assumptions. The model was analyzed through transfer functions, Bode plots, Nyquist loci, Nichols diagrams, RC-ladder approximation, sensitivity indicators, and biological interpretation tables.</p><p><strong>Results: </strong>The model predicts non-integer gain slopes approaching -20α dB per decade, phase plateaus near -90α degrees, and depressed Nyquist arcs whose depression increases as α decreases. The γ parameter controls the transition from passive leakage-dominated filtering to active or mixed active-passive regimes, while β modifies delayed ionic or kinetic effects. The RC-ladder approximation supports the physical realizability of the fractional element, although biological validation requires future fitting against patch-clamp and bioimpedance datasets.</p><p><strong>Conclusion: </strong>BioMem-FWK4α provides a compact and interpretable circuit framework for describing memory-rich membrane impedance and ion-channel-like dynamics. It does not replace Hodgkin-Huxley or Markov-state models for detailed nonlinear gating, but complements them by offering a reduced-order frequency-domain and analog-realizable representation of distributed membrane memory. Future work should validate the model using time-resolved patch-clamp, tissue bioimpedance, and voltage-dependent nonlinear parameter identification.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261484325"},"PeriodicalIF":2.9,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13530512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.","authors":"Muhammad Hamza Rafique Bhatti, Amir Aly, Asiya Khan, Shakil Awan","doi":"10.1177/11795972261481838","DOIUrl":"10.1177/11795972261481838","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) is a progressive neurodegenerative disorder of late life that causes cognitive and functional decline and substantial mortality. Machine learning (ML) is increasingly used to discover patterns in clinical and biomarker data that support earlier and more accurate AD detection.</p><p><strong>Objectives: </strong>This scoping review addresses three core research questions. First, we investigate recent trends in using machine-learning techniques to detect Alzheimer's disease using blood biomarkers. Second, we identify the blood biomarkers involved in Alzheimer's detection and evaluate how machine learning has been applied to improve the diagnostic capabilities of these biomarkers. Third, we highlight significant challenges associated with using machine learning for blood biomarker data in Alzheimer's detection and examine proposed advancements or solutions to handle these problems.</p><p><strong>Methods: </strong>In June 2025, we searched six academic databases to identify relevant papers on blood biomarkers and ML methods for Alzheimer's Disease. Search queries were developed based on our predefined research questions. Papers were then screened using defined inclusion and exclusion criteria, where titles, abstracts, and full texts of articles were systematically reviewed.</p><p><strong>Results: </strong>Following the screening approach, we selected 36 papers that fulfilled our inclusion and exclusion criteria. Through careful examination, we classified blood biomarkers into four types: transcriptomics, proteomics, multi-omic biomarkers, and general elemental blood biomarkers. Across these studies, proteomic blood biomarkers consistently emerged as significant indicators for Alzheimer's disease, including Alpha-2-Macroglobulin (A2M), Apolipoprotein E (ApoE), Eotaxin-3 (EOT3), plasma phosphorylated tau (p-tau 181), and neurofilament light chain (NfL). Furthermore, we explored challenges such as small sample sizes, lack of standardization, heterogeneity, and data imbalance.</p><p><strong>Conclusions: </strong>This review provides insights into how combining blood biomarkers with ML can enhance AD prediction. The review summarizes key challenges and identifies critical gaps for future research.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261481838"},"PeriodicalIF":2.9,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148841525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized High- Resolution Network for Accurate Facial Wrinkle Detection Using Harbor Seal Whiskers Optimization.","authors":"V Senthil Murugan, V Hemasree, Wulfran Fendzi Mbasso, Zokir Mamadiyarov","doi":"10.1177/11795972261477568","DOIUrl":"10.1177/11795972261477568","url":null,"abstract":"<p><strong>Background: </strong>Automated facial wrinkle detection is relevant to facial-ageing assessment, cosmetic analysis, dermatological screening, and personalized skincare. However, wrinkles are thin, low-contrast, and spatially irregular structures whose appearance may be affected by illumination, pose, skin texture, age, image quality, and demographic characteristics.</p><p><strong>Objective: </strong>This study proposes FWD-DHRN-HSWOA, a facial wrinkle-detection framework combining attentional image enhancement, high-resolution feature representation, and metaheuristic parameter optimization.</p><p><strong>Methods: </strong>Facial images were obtained from the FG-NET Aging Database and supplemented with study-specific wrinkle annotations because wrinkle labels are not native to FG-NET. Images were aligned, resized, normalized, and enhanced using Deep Attentional Guided Image Filtering. A Dynamic Lightweight High-Resolution Network was then used to preserve fine spatial information during wrinkle localization. Harbor Seal Whiskers Optimization was applied as an outer-loop procedure for tuning selected model parameters. The proposed method and implemented baselines were evaluated using the same data partitions, preprocessing conditions, and performance metrics.</p><p><strong>Results: </strong>Within the evaluated FG-NET-derived annotation setting, the proposed framework produced higher observed accuracy, precision, recall, F1-score, and specificity and lower RMSE than the implemented comparison models. These results reflect performance under the reported experimental setting and should not be interpreted as evidence of equivalent performance across unrepresented demographic groups or dedicated clinical wrinkle datasets.</p><p><strong>Conclusion: </strong>Combining attentional preprocessing, high-resolution feature extraction, and metaheuristic optimization shows promise for fine facial wrinkle localization. Nevertheless, external validation on dedicated wrinkle datasets with verified age, ethnicity, skin-tone, and acquisition-condition diversity is necessary before broad deployment.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261477568"},"PeriodicalIF":2.9,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13462493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148727005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Diagnostic Tool for Elbow Instability Combining 4D-CT Imaging and Mechanical Load Analysis.","authors":"Jonas Gehring, Fabian Forster, Markus Meindl, Martin Maerz, Michael Simon, Nina Renner, Michael Uder, Tobias Pogarell, Lars Braeuer, Mario Perl, Lucas Mangold","doi":"10.1177/11795972261472224","DOIUrl":"10.1177/11795972261472224","url":null,"abstract":"<p><strong>Background: </strong>Diagnosis of elbow instability in clinical practice commonly relies on manual stress testing by the physician with or without combination of fluoroscopy or ultrasound, which may result in variable force application and subjective interpretation. A standardized method for applying controlled mechanical loading during dynamic imaging may improve reproducibility.</p><p><strong>Objective: </strong>The objective of this study was to develop and perform initial cadaveric feasibility testing of a CT-compatible diagnostic device for device-guided assessment of elbow instability with simultaneous force and positional measurement.</p><p><strong>Design: </strong>Technical development study with initial cadaveric feasibility testing.</p><p><strong>Methods: </strong>Building upon the initial prototype the present paper, describes the development of MELBO the follow-up prototype with adjusted kinematics and improved stability, the validation of the force sensor is addressed, and the first cadaver test results are presented.</p><p><strong>Results: </strong>The MELBO diagnostic tool enabled a controlled device-guided elbow motion during 4D-CT acquisition and allowed simultaneous recording of force and positional data. The acquired dynamic CT datasets were suitable for qualitative assessment of elbow joint stability. No relevant device-related imaging artifacts were observed.</p><p><strong>Conclusion: </strong>The present study demonstrates the technical feasibility of device-guided simultaneous force and positional measurement during 4D-CT using the MELBO diagnostic tool. These findings provide the basis for further cadaveric studies and future clinical investigations aimed at quantitative assessment of elbow instability under standardized loading conditions.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261472224"},"PeriodicalIF":2.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13458129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-Enhanced Hybrid Bidirectional LSTM and Temporal Convolutional Network for Early Detection of Lung Cancer in Low-Dose CT Scans.","authors":"Benjamin Appiah Yeboah, Michael Asiedu Asare, Isaac Acquah, Kofi Ampomah Mensah, Mawusi Gbemavor-Assonhe","doi":"10.1177/11795972261472208","DOIUrl":"10.1177/11795972261472208","url":null,"abstract":"<p><strong>Objectives: </strong>We developed and evaluated a lightweight, interpretable, and computationally efficient hybrid deep learning model for multiclass classification of early lung cancer in low-dose CT scans, potentially deployable in resource-limited healthcare environments.</p><p><strong>Methods: </strong>A novel hybrid architecture was developed that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks, Temporal Convolutional Networks (TCNs), Efficient Channel Attention (ECA) blocks, and Local Interpretable Model-Agnostic Explanations (LIME). The model employed depthwise separable convolutions to reduce computational complexity. A multi-stream feature extraction framework was implemented to enhance interpretability and capture spatial-temporal patterns. The model was trained and validated on the IQ-OTH/NCCD dataset (version 2), containing 3,609 CT scan slices from 110 patients (1,097 original, 2,512 augmented), across three classes: normal, benign, and malignant. The dataset was split into training (60%), validation (30%), and testing (10%) subsets. Training was conducted using the AdamW optimizer for 16 epochs.</p><p><strong>Results: </strong>The model achieved 98.06% accuracy, 98.15% precision, 98.06% recall, and 98.04% F1-score, with a 99.88% AUC, a model size of 3.33 MB, and 279,561 parameters.</p><p><strong>Conclusion: </strong>The lightweight model achieves high diagnostic accuracy with computational efficiency, SHAP-based and LIME-based interpretability methods, enabling potential suitability for deployment in resource-constrained clinical settings.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261472208"},"PeriodicalIF":2.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13458125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluid-Structure Interaction Analysis of Hyoid Bone-Induced Compression on Carotid Artery Hemodynamics.","authors":"Behrad Nikbakhtian, Arshia Eskandari, Mahkame Sharbatdar, Aisa Rassoli","doi":"10.1177/11795972261472216","DOIUrl":"10.1177/11795972261472216","url":null,"abstract":"<p><p>The carotid bifurcation plays a crucial role in cerebral perfusion, and its hemodynamic behavior is influenced by external factors, including interactions with surrounding anatomical structures. This study investigates the impact of hyoid bone-induced compression on carotid artery hemodynamics using computational fluid dynamics (CFD) and fluid-structure interaction (FSI) modeling. The results reveal significant alterations in time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), velocity distribution, and Von-Mises stress, highlighting the biomechanical effects of external compression at various arterial locations. Compression of the common carotid artery results in a marked reduction in downstream TAWSS, dropping from 3.68 Pa to 1.70 Pa, and a substantial increase in OSI, reaching up to 0.46, suggesting disturbed flow patterns that may contribute to pathological vessel remodeling. In contrast, compression at the internal carotid artery leads to localized elevations in TAWSS, reaching approximately 6.90 Pa at the contact site and 7.80 Pa at the bifurcation, while OSI remains relatively low (around 0.22). Similarly, high oscillatory low magnitude shear index (HOLMES) levels decrease significantly after compression of the common carotid artery, from 1.23 Pa to 0.41 Pa, but increase when the internal carotid artery is compressed, peaking at 3.30 Pa at the contact site and 2.60 Pa downstream within the external carotid artery. Velocity streamline analysis demonstrates prominent vortex formation at the bifurcation, particularly in cases where direct compression occurs at this site, indicating disrupted and recirculating flow. Von-Mises stress analysis shows the highest stress concentration at the contact region across all cases. The lowest stress is observed with common carotid compression (approximately 0.6 MPa), while higher stresses occur in the bifurcation and internal carotid cases (around 1.5 MPa). The greatest mechanical stress, reaching approximately 1.9 MPa, is seen when the external carotid artery is compressed, indicating a higher risk of structural damage in this region. These results improve understanding of the effects of external anatomical interactions on carotid artery hemodynamics and motivate further investigation of their clinical significance. Future investigations should prioritize patient-specific modeling and in vivo validation to better assess the long-term vascular health impacts of hyoid bone-induced compression.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261472216"},"PeriodicalIF":2.9,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.","authors":"Yousra El Alaoui, Regina Padmanabhan, Marwa K Qaraqe, Adel Elomri, Halima El Omri, Ruba Y Taha, Sergio Crovella, Laoucine Kerbache, Abdelfatteh El Omri","doi":"10.1177/11795972261456588","DOIUrl":"10.1177/11795972261456588","url":null,"abstract":"<p><strong>Background: </strong>Acute Myelogenous Leukemia (AML) is a rapidly progressing blood and bone marrow cancer, prevalent among adults. Very low five-year survival rate, non-specific and ambiguous symptoms, and lack of potent screenings make early detection and prompt treatment crucial, especially for younger patients. The need for multiple tests to confirm AML and possible misdiagnosis often leads to multiple consultations before moving to the next stage of investigations. This can create operational bottlenecks in the hematology department, leading to further delays in the diagnostic pathway.</p><p><strong>Objective: </strong>To develop a lightweight artificial intelligence (AI) model using complete blood count (CBC) data for early AML detection and development of a decision support system (DSS) for triage support.</p><p><strong>Methods: </strong>The data for this study were retrieved retrospectively from the National Center for Cancer Care and Research (NCCCR), Qatar (2016-2022). We used 510 CBC data records of AML and non-AML individuals. Statistical analysis of CBC features (mean ± SD) for AML and control patients was performed, followed by principal component analysis (PCA) to assess the predictive ability of CBC alone.</p><p><strong>Results: </strong>Rigorous feature selection and model tuning resulted in a predictive diagnostic Support Vector Machine (SVM) model to alleviate delays in the AML care pathway. Employing a five-fold cross-validation approach, achieved an accuracy of 96.4% (S.D. 0.029) for test set and 80% (S.D. 0.036) for validation set. This 80% accuracy on the validation set, with high sensitivity (100% recall) but lower precision (77.1%), represents an acceptable triage trade-off, although it may increase follow-up referrals and workload. The developed model showed encouraging performance when used to detect AML using CBCs taken up to 1-year prior diagnosis.</p><p><strong>Conclusion: </strong>This study emphasizes that, with routine CBC data, we can enable better patient screening and referral in the initial stages of triage through a cost-effective, AI-based decision support system that provides complementary support to doctors for timely AML detection.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261456588"},"PeriodicalIF":2.9,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities and Limitations of Online and Web-Based Survey Data Collection for Evidence-Based Policymaking.","authors":"Kalpana Singh","doi":"10.1177/11795972261467821","DOIUrl":"10.1177/11795972261467821","url":null,"abstract":"<p><p>In an increasingly digital and fast-paced policy environment, the rise of online and web-based survey methods offers significant potential for strengthening evidence-based policymaking. This commentary critically explores the methodological opportunities and inherent limitations of these digital data-collection approaches, particularly within policy domains such as health and social governance. While online surveys provide notable advantages in terms of speed, scalability, adaptability, and cost efficiency, they also present substantial methodological and ethical challenges, including coverage errors, selection bias, measurement inconsistencies, and concerns about data integrity. The discussion highlights that the credibility of policy-relevant evidence depends not solely on data volume or immediacy but on methodological robustness, transparent documentation, and careful communication of uncertainty. Integrating digital survey modes with traditional probability-based and administrative data sources is therefore advocated as a balanced and sustainable approach to generating reliable evidence for policy decisions in the digital age. This commentary further argues that a fit-for-purpose approach where survey methods are aligned with the intended policy objective, required precision, and decision context should serve as a central organizing principle for evaluating the appropriateness of digital evidence in contemporary policymaking.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261467821"},"PeriodicalIF":2.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MnL-TWA: Manifold Learning Approach for T-Wave Alternans Detection in Ambulatory Environments.","authors":"Lidia Pascual-Sánchez, Rebeca Goya-Esteban, Manuel Blanco-Velasco","doi":"10.1177/11795972261463531","DOIUrl":"10.1177/11795972261463531","url":null,"abstract":"<p><strong>Background: </strong>T-wave alternans (TWA) refers to variations in the ventricular repolarization pattern observed on the ECG, which has been associated with cardiac instability and an increased risk of sudden cardiac death. Recently, machine learning (ML) methods have been developed for TWA detection, but their black-box nature limits interpretability.</p><p><strong>Objectives: </strong>To address this gap, we propose manifold learning (MnL) to enhance the explainability of these learning models while maintaining TWA detection effectiveness.</p><p><strong>Methods: </strong>We fine-tuned nonlinear dimension reduction techniques such as Uniform Manifold Approximation and Projection (UMAP), Isometric Mapping (Isomap), and autoencoders (AE) in combination with ML methods, namely K-nearest neighbors (KNN), random forest (RF), and neural networks (NN). Performance was evaluated using mean and standard deviation across patient-wise permutations.</p><p><strong>Results: </strong>In the design stage, the AE-based NN effectively retained essential discriminative information (F1-score 92.1 ± 2.4 %). MnL-generated spaces consistently revealed that misclassifications primarily lie close to the decision boundary and are predominantly associated with lower TWA voltages, which are more dispersed within the space. For ambulatory TWA detection, Isomap combined with RF and the AE-based NN achieved performance comparable to using the complete set of features derived from established TWA analysis methods (F1-score 78.5 ± 6.4 % and 77.9 ± 5.4 %, respectively), including spectral, time-domain, and correlation-based descriptors. The latent space visualization shows that predictions that ultimately become detections are located farther away from the decision boundary.</p><p><strong>Conclusion: </strong>MnL-generated spaces provide valuable insights into how classification models differentiate between TWA and non-TWA instances, as well as the patterns in TWA event amplitudes. This approach helps bridge the gap between performance and transparency, supporting more clinically reliable TWA detection.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261463531"},"PeriodicalIF":2.9,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13329019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148392133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Unified Cardiovascular-Pupillary Model for Interpreting Pupil Size Variability as an Autonomic Marker.","authors":"Kevin Hung, Gary Man-Tat Man, Daniel Hung-Kay Chow","doi":"10.1177/11795972261463516","DOIUrl":"10.1177/11795972261463516","url":null,"abstract":"<p><p><b>Background:</b> Pupil size variability (PSV) has emerged as a potential non-contact indicator of autonomic nervous system (ANS) function; however, its physiological origins remain unclear. This study aims to develop a computational model to investigate the physiological foundations of PSV and assess how frequency-domain indices reflect cardiovascular autonomic balance. <b>Methods:</b> We integrated a well-established cardiovascular regulation model with a biomechanical pupillary muscle plant. The model simulates PSV alongside heart rate variability (HRV) by transmitting respiratory and baroreflex inputs through an indirect neural pathway to the pupillary muscles. Frequency-domain analyses were conducted to compare simulated PSV and HRV across different autonomic states. <b>Results:</b> Simulations suggest that PSV arises from respiratory and baroreceptor inputs, with its classical range-nonlinearity (RNL) property emerging naturally from iris biomechanics. The model reproduces key physiological behaviors, including inspiration-linked dilation and parasympathetic modulation. Frequency-domain analyses reveal low- and high-frequency components in PSV that are similar to those found in HRV. However, the magnitudes of these PSV components depend heavily on the underlying autonomic state and the mean pupil size. <b>Conclusion:</b> These findings provide a mechanistic framework for interpreting PSV as a cardiovascular autonomic biomarker. This ultimately supports the development of non-invasive, wearable, and eye-based ANS monitoring systems.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261463516"},"PeriodicalIF":2.9,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}