Biomedical Engineering Letters最新文献_第2页

Virtual histological staining: toward standardization and clinical translation. 虚拟组织学染色：走向标准化和临床转化。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-19 eCollection Date: 2026-07-01 DOI: 10.1007/s13534-026-00597-6

Santanu Misra, Chiho Yoon, Eunwoo Park, Sampa Misra, Chulhong Kim, Byullee Park

{"title":"Virtual histological staining: toward standardization and clinical translation.","authors":"Santanu Misra, Chiho Yoon, Eunwoo Park, Sampa Misra, Chulhong Kim, Byullee Park","doi":"10.1007/s13534-026-00597-6","DOIUrl":"https://doi.org/10.1007/s13534-026-00597-6","url":null,"abstract":"Histochemical staining is indispensable for histopathological diagnosis, enabling interpretation of cellular and tissue architecture and lesion patterns. Deep learning-based virtual staining has rapidly expanded across label-free and stain-to-stain settings by learning data-driven transformations to synthesize stain-like contrast or additional stain representations, with the potential to reduce turnaround time and the need for additional tissue sections and repeated staining. Yet standardization remains limited across studies. Inconsistent acquisition, data curation, and preprocessing, model configuration, and evaluation reduce reproducibility, hinder fair cross-study comparison, and weaken clinical translation. In this review, we present a standardization blueprint that organizes the field of modality-specific data construction and model design to domain shift, evaluation, safety, and clinical translation. We propose a minimum reporting checklist to support consistent disclosure of datasets, protocols, and evaluation settings for reproducible benchmarking. Finally, we discuss clinical translation and regulatory considerations by situating virtual staining within real pathology workflows, clarifying why full replacement is not yet routine in practice, and highlighting key evidence gaps that must be addressed for safe, credible deployment. Collectively, these contributions advance standardization in deep learning-based virtual staining by enabling more reproducible reporting, more comparable benchmarking, and more clinically grounded assessment.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 4","pages":"855-882"},"PeriodicalIF":3.4,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainability guided feature selection for speech emotion recognition using SHAP and permutation importance. 基于SHAP和排列重要性的可解释性语音情感识别特征选择。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-18 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00592-x

S I M M Raton Mondol, Sangmin Lee

{"title":"Explainability guided feature selection for speech emotion recognition using SHAP and permutation importance.","authors":"S I M M Raton Mondol, Sangmin Lee","doi":"10.1007/s13534-026-00592-x","DOIUrl":"10.1007/s13534-026-00592-x","url":null,"abstract":"Speech Emotion Recognition (SER) systems commonly rely on high-dimensional handcrafted acoustic feature sets, which increase computational complexity, risk overfitting, and reduce interpretability. To address these challenges, we propose an explainability-driven, redundancy-aware feature selection framework that integrates SHAP-based attribution with permutation-based performance importance through a unified rank-sum criterion. A correlation-controlled redundancy penalty is further introduced to mitigate multicollinearity while preserving acoustically meaningful descriptors. The framework operates entirely in the original feature space, enabling transparent and interpretable model refinement. Experiments were conducted on four widely used emotional speech corpora (CREMA-D, RAVDESS, SAVEE, and TESS) under within-corpus evaluation settings. To assess the generalizability of the proposed framework, experiments were performed using multiple classifiers, including XGBoost, Support Vector Machine (SVM), and Random Forest (RF). Using the XGBoost classifier, the proposed framework achieved accuracies of 0.600 on CREMA-D, 0.752 on RAVDESS, 0.726 on SAVEE, and 0.998 on TESS, while reducing the feature space by up to 40-70%. Macro-F1 and UAR metrics exhibit similar trends, confirming stable performance gains. Additional experiments using SVM and RF further indicate that the proposed feature selection framework generalizes across different classifiers while maintaining competitive performance. The findings indicate that explainability can serve not only as a post-hoc interpretation tool but as an active optimization mechanism for compact and interpretable SER models.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1345-1356"},"PeriodicalIF":3.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medication-specific risk of atrial fibrillation in patients with coronary artery disease. 冠心病患者房颤的药物特异性风险

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-18 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00589-6

Taehyun Kim, Gayeon Ryu, Ye Eun Choi, Hangsik Shin

{"title":"Medication-specific risk of atrial fibrillation in patients with coronary artery disease.","authors":"Taehyun Kim, Gayeon Ryu, Ye Eun Choi, Hangsik Shin","doi":"10.1007/s13534-026-00589-6","DOIUrl":"10.1007/s13534-026-00589-6","url":null,"abstract":"Purpose: Cardiovascular medications used in coronary artery disease (CAD) can potentially modulate the risk of incident atrial fibrillation (AF). However, comprehensive data regarding medication-specific AF risk in the CAD population remain limited. This study aimed to identify prevalent medications prescribed to patients with CAD and evaluate their independent associations with AF risk. In this retrospective cohort study of 157,750 patients with CAD and related comorbidities, CAD-predominant medications were identified based on a frequency analysis of prescriptions in the overall cohort. Among 99,370 patients who received at least one such medication, associations with incident AF were evaluated using extended Cox proportional hazards models with time-varying covariates. Overall, 30 CAD-predominant medications were identified across nine pharmacological classes. In multivariable Cox proportional hazards models, six medications were significantly associated with AF risk, of which one was associated with a lower risk and five with a higher risk. Ezetimibe/rosuvastatin was associated with reduced AF risk, whereas diltiazem HCl, torasemide, heparin sodium, bisoprolol fumarate, and norepinephrine were associated with increased risk. Torasemide showed the strongest positive association. AF risk demonstrated heterogeneity across different CAD medications. Antilipidemic agents were associated with a lower AF risk, whereas certain diuretics, antianginal agents, antihypertensive, and vasoactive agents were associated with a higher risk. These results may have implications for individualized pharmacotherapy and AF prevention in patients with CAD.Supplementary information: The online version contains supplementary material available at https://doi.org/10.1007/s13534-026-00589-6.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1333-1343"},"PeriodicalIF":3.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of breast cancer metastasis based on Tri-training using Dempster-Shafer theory. 基于Dempster-Shafer理论的三训练预测乳腺癌转移。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-18 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00595-8

Jingdong Yang, Yingfei Huang, Yuhang Lu, Junjie Zhang, Yan Shen, Xiaohong Fu

{"title":"Prediction of breast cancer metastasis based on Tri-training using Dempster-Shafer theory.","authors":"Jingdong Yang, Yingfei Huang, Yuhang Lu, Junjie Zhang, Yan Shen, Xiaohong Fu","doi":"10.1007/s13534-026-00595-8","DOIUrl":"10.1007/s13534-026-00595-8","url":null,"abstract":"Objective: Machine learning applied to the clinical prediction of breast cancer mainly deals with a single view feature, so class imbalance, low classification accuracy and poor generalization performance may occur.Methods: Addressing the aforementioned problems, this study proposes a semi-supervised learning framework for predicting breast cancer metastasis. An integrated over-sampling and under-sampling approach is employed to construct multiple diverse weak classifiers, aiming to mitigate prediction bias towards the majority class and enhance generalization performance. By improving the classical Dempster-Shafer theory, the credibility of evidence sources is dynamically adjusted based on the conflict between them, thereby enhancing the accuracy of multi-source feature fusion. This improved DS theory is integrated with the Tri-training collaborative training strategy. Credibility thresholds are used to generate pseudo-labels from unlabeled data, which expands the labeled dataset, reduces prediction conflicts among multiple models, and improves both prediction accuracy and generalization. Furthermore, the SHAP interpretability method is adopted for feature importance analysis, aiming to identify potential risk factors and provide technical support for the clinical diagnosis of breast cancer.Results: The 304 clinical cases of breast cancer are from Shanghai Pudong New Area Gongli Hospital. The Accuracy, Precision, Recall, F1-score, and AUC on threefold cross-validation are 94.41, 94.44, 93.51, 93.88 and 93.5% respectively.Conclusions: The proposed model outperforms conventional supervised machine learning baselines, providing effective technical support for clinical breast cancer diagnosis.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1357-1379"},"PeriodicalIF":3.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of physical function using a single IMU during walking in stroke patients. 脑卒中患者行走时使用单一IMU进行身体功能分类。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-18 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00591-y

Sumin Yang, Jongman Kim, Inchan Youn, Seung-Jong Kim, Sungmin Han

{"title":"Classification of physical function using a single IMU during walking in stroke patients.","authors":"Sumin Yang, Jongman Kim, Inchan Youn, Seung-Jong Kim, Sungmin Han","doi":"10.1007/s13534-026-00591-y","DOIUrl":"10.1007/s13534-026-00591-y","url":null,"abstract":"Stroke leads to gait impairment and reduced physical function, making accurate functional assessment essential for effective rehabilitation planning. However, conventional clinical scales may be limited by discrete scoring and examiner dependency, whereas laboratory-based motion analysis systems are costly and difficult to implement routinely. This study aimed to classify physical function in stroke patients using gait features derived from a single shank-mounted inertial measurement unit (IMU) and to compare the classification performance with that of a motion capture (MoCap) system. Thirty-two stroke patients completed the Short Physical Performance Battery (SPPB), and participants were divided into low- and high-physical-function groups using a cutoff score of 10. MoCap-based features included spatiotemporal gait parameters and joint kinematics, whereas IMU-based features included descriptive statistical, gait-related, frequency, and variability features extracted from raw sensor signals. A filtering-based feature selection procedure was applied, and the top 40 features were selected using mutual information. Six machine learning models were evaluated using leave-one-out cross-validation. Among the evaluated models, the IMU-based linear discriminant analysis model achieved an accuracy of 0.91, which was comparable to that of the MoCap-based XGBoost model (0.94). These findings indicate that a single shank-mounted IMU may provide sufficient gait information for objective classification of physical function in stroke patients, supporting its use as a practical and low-burden assessment tool in stroke rehabilitation.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1309-1318"},"PeriodicalIF":3.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive consistency co-optimization network for semi-supervised medical image segmentation. 半监督医学图像分割的自适应一致性协同优化网络。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-18 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00590-z

Ruihua Liu, Yanwei Liu, Jiangyu Liao, Xinyu Liu, Ting Xie, Yangyang Zou, Yijie Chen

{"title":"An adaptive consistency co-optimization network for semi-supervised medical image segmentation.","authors":"Ruihua Liu, Yanwei Liu, Jiangyu Liao, Xinyu Liu, Ting Xie, Yangyang Zou, Yijie Chen","doi":"10.1007/s13534-026-00590-z","DOIUrl":"10.1007/s13534-026-00590-z","url":null,"abstract":"Semi-supervised learning alleviates the reliance on extensive labeled datasets, offering significant potential for addressing complex tasks in medical image analysis. However, existing methods often struggle to fully harness the complementary strengths of diverse network architectures, resulting in limited feature extraction from labeled data and underutilization of information from unlabeled data. These challenges are particularly pronounced in segmenting organs with intricate anatomical structures and poorly defined boundaries, leading to inaccuracies that degrade overall recognition performance. This study introduces the adaptive consistency co-optimization network (ACCO-Net) to address these limitations. For labeled data, the adaptive selection generation module (ASG) generates enhanced samples and labels by selecting predictions with lower losses following both weak and strong augmentations, enhancing learning in complex regions. For unlabeled data, the adaptive consistency optimization module (ACO) combines feature consistency feedback with dynamic dual optimization to enhance feature extraction within the encoder. This module also incorporates boundary segmentation guidance using pretrained level set contours, enabling accurate restoration of fine details. Experiments on the ACDC and PROMISE12 datasets demonstrate that ACCO-Net achieves SOTA performance. The proposed framework effectively leverages the complementary strengths of different network architectures to improve segmentation accuracy for organs with complex anatomical structures and indistinct boundaries.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1381-1394"},"PeriodicalIF":3.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of R-R interval normalization on frequency domain analysis of heart rate variability during sleep. R-R间隔归一化对睡眠时心率变异性频域分析的影响。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-16 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00596-7

Jonghyun Hong, Jungmin Koh, Jinyoung Kim, Hyunchan Ryu, Dahye Lee, Ji Ho Choi, Hyun Jae Baek, Heenam Yoon

{"title":"Effect of R-R interval normalization on frequency domain analysis of heart rate variability during sleep.","authors":"Jonghyun Hong, Jungmin Koh, Jinyoung Kim, Hyunchan Ryu, Dahye Lee, Ji Ho Choi, Hyun Jae Baek, Heenam Yoon","doi":"10.1007/s13534-026-00596-7","DOIUrl":"10.1007/s13534-026-00596-7","url":null,"abstract":"To investigate the effect of R-R interval (RRI) normalization using different scaling factors ([Formula: see text]) on frequency-domain heart rate variability (HRV) parameters during sleep, and determine whether normalization improves the differentiation of sleep stages and enhances the accuracy of slow-wave sleep (SWS) detection. We analyzed the overnight electrocardiogram data of 130 participants. RRIs were normalized using a proposed z-score method with scaling factors (n = 1-4 in [Formula: see text]). Frequency-domain HRV parameters were extracted using raw and normalized RRIs within a 5-min time window shifted by 30 s. HRV parameters were statistically compared across sleep stages (wakefulness, light sleep, SWS, and rapid eye movement sleep), and the SWS detection performance was evaluated using receiver operating characteristic analysis. RRI normalization significantly magnified differences in HRV parameters across sleep stages. HF, which reflects parasympathetic activity, showed a markedly clearer separation between SWS and other stages with normalization (p < 0.05) than with the raw RRI. Consequently, the SWS detection performance was significantly improved. The area under the curve for HF-based SWS detection increased from 0.50 (raw RRI) to a maximum of 0.86 (with [Formula: see text] normalization). HRV parameters derived from normalized RRI reveal the hierarchical organization of autonomic nervous activity across sleep stages, particularly parasympathetic dominance in SWS. This method significantly improves SWS detection accuracy and provides a robust foundation for refined HRV-based sleep monitoring systems. It can be utilized in real-time health monitoring systems to accurately assess sleep stages, stress, and fatigue levels.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1395-1406"},"PeriodicalIF":3.4,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phase-change perfluorocarbon nanodroplets enable spatiotemporal control of ultrasound and photoacoustic imaging and therapy. 相变全氟碳纳米液滴实现了超声和光声成像和治疗的时空控制。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-06-01 eCollection Date: 2026-07-01 DOI: 10.1007/s13534-026-00587-8

Euisuk Chung, Robert J Nikolai, Seoyoon Song, Stanislav Y Emelianov

{"title":"Phase-change perfluorocarbon nanodroplets enable spatiotemporal control of ultrasound and photoacoustic imaging and therapy.","authors":"Euisuk Chung, Robert J Nikolai, Seoyoon Song, Stanislav Y Emelianov","doi":"10.1007/s13534-026-00587-8","DOIUrl":"https://doi.org/10.1007/s13534-026-00587-8","url":null,"abstract":"Phase-change perfluorocarbon nanodroplets (PFCnDs) are submicrometer, liquid-core agents capable of generating strong ultrasound (US) and photoacoustic contrast following external stimulation. This selective vaporization allows site-specific signal generation and facilitates imaging beyond the vascular compartment. The behavior of PFCnDs is determined by a thermodynamic landscape in which Laplace pressure and nucleation kinetics jointly influence vaporization thresholds and post-vaporization outcomes, including persistent gas bubble formation or rapid recondensation. This review summarizes the mechanistic framework underlying acoustic droplet vaporization, optical droplet vaporization, and combined optical-acoustic droplet vaporization of PFCnDs. These phase-change mechanisms are discussed in relation to formulation variables such as perfluorocarbon species, size distribution, shell mechanics, and absorber loading, as well as exposure conditions including acoustic pressure, frequency, pulse structure, and optical fluence. The review further examines how these coupled parameters govern vaporization dynamics, imaging persistence, and associated bioeffects. Imaging applications, including contrast-enhanced US, extravascular and lymphatic imaging, super-resolution, and multiplexed approaches that leverage vaporization-recondensation behavior, are surveyed. Therapeutic applications, such as drug delivery and barrier-opening techniques, are also examined alongside clinical translation priorities, including manufacturing, in vivo fate verification, reproducible vaporization, and standardized safety dosimetry.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 4","pages":"837-853"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cardiovascular digital twin personalization using arterial blood pressure-driven optimization of a Windkessel-baroreflex model. 心血管数字双胞胎个性化使用动脉血压驱动优化的windkessel - barreflex模型。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-05-20 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00584-x

Rahmafatin Nurul Izza, Frederique Jos Vanheusden, Fauzan Syarif Nursyafi, Ki Moo Lim

{"title":"Cardiovascular digital twin personalization using arterial blood pressure-driven optimization of a Windkessel-baroreflex model.","authors":"Rahmafatin Nurul Izza, Frederique Jos Vanheusden, Fauzan Syarif Nursyafi, Ki Moo Lim","doi":"10.1007/s13534-026-00584-x","DOIUrl":"10.1007/s13534-026-00584-x","url":null,"abstract":"Cardiovascular digital twins have emerged as powerful tools for modeling patient-specific cardiovascular physiology and supporting clinical decision-making. However, their practical adoption remains limited due to reliance on invasive or imaging-based data and computationally intensive personalization procedures. This study presents a cardiovascular digital twin personalization framework that integrates circulation system hemodynamic parameter optimization with arterial baroreflex gain calibration using only non-invasive arterial blood pressure measurements. A twelve-compartment Windkessel-baroreflex model was personalized in two steps. First, left ventricular end-systolic elastance and global vascular resistance and compliance scaling were optimized from a single resting arterial pressure waveform. After this, arterial baroreflex gains for total peripheral resistance and venous unstressed volume were tuned from beat-by-beat responses during head-up tilt and subject characteristics. In 10 PhysioNet subjects, the personalized models reproduced resting systolic, diastolic, and mean arterial pressures with absolute errors below 5 mmHg. The model also reproduced the main features of heart-rate and arterial-pressure responses during rapid and slow tilt, with better quantitative agreement for heart rate than for mean arterial pressure. These findings support the feasibility of constructing personalized cardiovascular digital twins from non-invasive arterial pressure recordings, while indicating the need for further refinement and broader validation before reliable subject-specific pressure prediction can be claimed.Supplementary information: The online version contains supplementary material available at https://doi.org/10.1007/s13534-026-00584-x.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1261-1271"},"PeriodicalIF":3.4,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-based multi-contrast windowing for task-specific medical image visualization. 基于深度学习的多对比度窗口医学图像可视化。

IF 3.4 4区医学

Biomedical Engineering Letters Pub Date : 2026-05-13 eCollection Date: 2026-09-01 DOI: 10.1007/s13534-026-00585-w

Jangho Kwon, Kihwan Choi

{"title":"Deep learning-based multi-contrast windowing for task-specific medical image visualization.","authors":"Jangho Kwon, Kihwan Choi","doi":"10.1007/s13534-026-00585-w","DOIUrl":"10.1007/s13534-026-00585-w","url":null,"abstract":"Windowing is the process in which the pixel values of an image are remapped to enhance the contrast of the regions of interest. For example, the width and level of the contrast window are adjusted to change the dynamic range and brightness of the CT image accordingly. Although deep learning is widely applied to analyzing medical images, selecting contrast windows for deep learning-based medical image analysis has not been fully explored. In this paper, we propose a data-driven windowing method which learns and suggests multi-contrast windows for the subsequent segmentation models to improve the prediction performance. The proposed method also provides a contrast-enhanced image to visualize which windows are relatively important for the subsequent deep learning model's prediction task. Through the experimental studies including liver tumor segmentation in CT images and abdominal organ/brain tumor segmentation in MRI images, we demonstrate that our multi-contrast windowing method enables subsequent models to improve the segmentation accuracy along with task-specific contrast-enhanced images.","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"16 5","pages":"1287-1298"},"PeriodicalIF":3.4,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0