IEEE Open Journal of Engineering in Medicine and Biology最新文献

{"title":"When Kids Radiate: Low-Resolution Thermography for Total Energy Expenditure Estimation in Pediatric Patients - A Proof of Concept","authors":"Sofiane Beloucif;Mario Francisco Munoz;Kevin Albert;Louise Faineant;Rita Noumeir;Philippe Jouvet","doi":"10.1109/OJEMB.2026.3667036","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667036","url":null,"abstract":"<italic>Goal:</i> Remote metabolic monitoring is a growing field in pediatric care, aiming to reduce invasive procedures while ensuring continuous assessment. However, clinical adoption remains limited by occlusions, poor image quality, and the scarcity of annotated data. In this study, we propose a framework based on deep learning to estimate total energy expenditure (TEE) in pediatric patients using low-resolution thermography. Our pipeline uses a UNet segmentation model trained to isolate anatomically relevant regions despite visual noise and occlusions. Radiative heat transfer computations are then applied to derive energy expenditure metrics. We tested our method in a cohort of 116 pediatric patients, achieving a mean TEE of 1547 kcal/m<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>/day and a mean absolute error of 279 kcal/m<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>/day. These results highlight the feasibility of thermography as a noninvasive, scalable alternative for metabolic monitoring in Pediatric Intensive Care Units (PICUs), especially in data-constrained environments.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"47-53"},"PeriodicalIF":2.9,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11407455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440559","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":"Contactless Sleep Staging With Radar: A Transfer Learning Approach","authors":"Daniel Krauss;Robert Richer;Nils Albrecht;Jelena Jukic;Carlos Herrera Krebber;Paul Zwiessele;Alexander German;Alexander Koelpin;Martin Regensburger;Jürgen Winkler;Bjoern M. Eskofier","doi":"10.1109/OJEMB.2026.3667047","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667047","url":null,"abstract":"Accurate sleep monitoring is essential to assess sleep quality and diagnose sleep disorders. Although sleep laboratories provide precise assessments, they are expensive, labor-intensive, and unsuitable for long-term or large-scale monitoring. Radar-based sensing offers a fully contactless alternative, enabling unobtrusive real-world sleep monitoring. However, the lack of large, labeled datasets has limited the development of robust sleep stage classification models. We address this with transfer learning to improve classification accuracy and generalization to unseen participants within the radar cohort. An LSTM model was pretrained on movement, HRV, and respiratory features from the MESA Sleep dataset (<inline-formula><tex-math>$&gt;$</tex-math></inline-formula>1,100 participants) and fine-tuned using radar data from 44 synchronized polysomnography recordings. Transfer learning increased the Matthews Correlation Coefficient from 0.25 to 0.47 (five-class staging), particularly for Wake, N3, and REM sleep. Future work should explore domain-adaptation across modalities and cohorts. Our results highlight the potential of radar-based sleep analysis for scalable, contactless long-term sleep monitoring.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"54-62"},"PeriodicalIF":2.9,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440582","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":"Distinguishing Gait Patterns in PD Patients Under Different Treatments via Recurrence Plots and Vision Transformer Fusion","authors":"Vasileios Skaramagkas;Georgios Karamanis;Iro Boura;Chariklia Chatzaki;Cleanthe Spanaki;Zinovia Kefalopoulou;Manolis Tsiknakis","doi":"10.1109/OJEMB.2026.3667045","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667045","url":null,"abstract":"<italic>Goal:</i> This study aims to develop an innovative gait analysis framework using recurrence plots (RPs) to differentiate gait patterns between Parkinson's disease (PD) patients under varying treatment regimes and healthy individuals. <italic>Methods:</i> Pressure sensor data were transformed into RPs and analyzed using a Vision Transformer (ViT) model with multiple fusion strategies. To address class imbalance, a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) was employed to generate synthetic gait data. Four ViT-based fusion architectures were investigated and evaluated across multi-class and binary classification tasks. <italic>Results:</i> The dual ViT stream with late fusion achieved the highest accuracy in multi-class classification (94.58%), while the cross-attention fusion model outperformed others in binary classification tasks. <italic>Conclusions:</i> The findings indicate that gait characteristics captured via RPs can effectively distinguish between PD patients under different treatments and healthy controls. This approach provides a data-driven pathway for objective and individualized assessment of PD therapies, potentially supporting improved clinical decision-making.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"63-69"},"PeriodicalIF":2.9,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440581","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":"2025 Index IEEE Open Journal of Engineering in Medicine and Biology Vol. 6","authors":"","doi":"10.1109/OJEMB.2026.3656806","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3656806","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"605-627"},"PeriodicalIF":2.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11360594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026363","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":"3D Engineered Biomimetic Platform for Characterization of Collective Invasion, Tumor Emboli Formation, and Lymphatic Dissemination","authors":"Caroline Way;Ralph F. Erdmann;Larisa Gearhart-Serna;Pritha Pai;Natalia Roman;Bruce Klitzman;Gayathri R. Devi","doi":"10.1109/OJEMB.2026.3666977","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3666977","url":null,"abstract":"<italic>Goal:</i> Emerging evidence in diverse tumor types establishes a link between lymphatic dissemination and collective tumor cell invasion. To simulate the biomechanical features of the tumor-lymphatic microenvironment, we developed a 3D tumor-lymphatic architecture biomimetic (T-LAB) platform. <italic>Methods:</i> Mathematical and computational fluid dynamics modeling were used to determine the fluid flow, oscillatory flow-induced shear stress, and system pressure in the 3D-printed macrofluidics platform. <italic>Results:</i> Various human breast cancer cell lines and human dermal lymphatic endothelial cells (HDLEC) were seeded on a matrix in the T-LAB and imaged for up to 96 h to assess cell morphology, viability, migration, and invasion. Co-culture of inflammatory breast cancer cells with HDLEC in the T-LAB, determined to simulate the fluidic properties of the tumor lymphatic microenvironment, demonstrated tumor cell clusters/emboli formation and collective invasion similar to the clinicopathological features observed in patients. <italic>Conclusions:</i> The 3D T-LAB model developed here can be used to culture any type of tumor cell to study topographical features that impact tumor-lymphatic interface, collective invasion, and lymphatic dissemination.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"119-127"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665498","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":"Cuffless Noninvasive Continuous Blood Pressure Monitoring Using Superficial Temporal Arterial Tonometry","authors":"Ge Zhu;Zifan Jiang;Gary Strangman;Yihao Zheng;Quan Zhang","doi":"10.1109/OJEMB.2026.3667451","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667451","url":null,"abstract":"<italic>Objective:</i> Continuous monitoring of blood pressure (BP) is a key parameter for cardiovascular assessment and hemodynamics monitoring. Current noninvasive methods are limited by frequent calibration, motion and environmental artifacts, and delayed response to rapid BP changes. In perioperative and critical-care settings, even short-duration hypotensive episodes and rapid BP lability have been associated with adverse outcomes, motivating technologies with high temporal fidelity. This study introduces a noninvasive blood pressure monitoring technique using superficial temporal artery tonometry (STAT), which employs a biomechanics-based transfer function to improve accuracy, reduce calibration requirements, and detect rapid BP changes in dynamic conditions. <italic>Methods:</i> Twenty-nine recording sessions of continuous BP monitoring were collected in human subjects (<italic>n</i> <inline-formula><tex-math>$=$</tex-math></inline-formula> 10) during rest and during handgrip-induced BP fluctuations. Measurements were recorded simultaneously using the STAT method and compared to a noninvasive reference device (Finapres/Finometer volume-clamp) and Pulse Transit Time (PTT) baseline (derived from timing features) method. <italic>Results:</i> Using the Finapres/Finometer as a noninvasive reference, our method achieved a mean absolute difference (MAD) of 4.8 <inline-formula><tex-math>$ pm $</tex-math></inline-formula> 2.2 mmHg during rest and 6.5 <inline-formula><tex-math>$ pm $</tex-math></inline-formula> 3.4 mmHg during handgrips, significantly outperforming PTT, especially under dynamic conditions. <italic>Conclusion:</i> BP monitoring with STAT and its biomechanics-based transfer function achieved improved detection of rapid BP fluctuations, and higher accuracy than PTT under dynamic conditions. <italic>Significance:</i> STAT with biomechanics-based modeling enables real-time, robust noninvasive BP monitoring, overcoming calibration, motion, and detection limitations of current methods.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"113-118"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606247","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":"Clinical Applications of Deep Learning for Glottal Area Segmentation and Glottal Area Waveform Feature Computation","authors":"Ying-Chang Wu;Sheng-Fu Liang;Ming-Chi Cheng;Hui-Chen Su;Cheng-Ming Hsu","doi":"10.1109/OJEMB.2026.3684089","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3684089","url":null,"abstract":"<italic>Goal:</i> Analysis of the glottal area during vocal fold vibration has gained increasing attention. However, traditional analysis requires manual, frame-by-frame glottal area annotation to compute the glottal area waveform, a time-consuming, and error-prone process. <italic>Methods:</i> This study proposes an automated system for glottal area segmentation and glottal area waveform feature extraction from 36 videostroboscopy recordings of 23 patients with vocal fold nodules. The system integrates YOLO and U-Net architecture for glottis detection and segmentation. Subject-independent 5-fold cross-validation was performed on 5017 annotated frames. <italic>Results:</i> The system achieved an average Intersection over Union of 92.8%, and a Dice Similarity Coefficient of 95.8%, substantially outperforming thresholding and edge-based baselines. Computation time was reduced by 27.7 -folds compared with manual method. Applied to 14 patients undergoing voice treatment, the system detected consistent trends in glottal area dynamics post-treatment. <italic>Conclusion:</i> The system enhances efficiency and accuracy of glottal area waveform analysis and demonstrates clinical utility for laryngeal assessment.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"158-164"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11481763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796161","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":"Augmented State-Space Modeling and Control of Latent Arousal States Under Inhibitory and Excitatory Conditions","authors":"Hamid Fekri Azgomi;Anan Yaghmour;Rose T. Faghih","doi":"10.1109/OJEMB.2026.3672470","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3672470","url":null,"abstract":"<italic>Goal:</i> In modern high-stress environments, effectively regulating cognitive arousal, through enhancement to boost engagement or inhibition to manage excessive stress, is essential for maintaining mental well-being and optimizing human performance. Hence, this study extends existing state-space models by integrating time-varying parameters and disturbance inputs for enhanced representation of arousal dynamics inferred from skin conductance. <italic>Methods:</i> We augmented nominal models with time-varying parameters, then developed a recursive Bayesian estimator for state tracking. Simulation-based validation was performed using skin conductance data from six participants, drawn from an experimental dataset of noninvasive wrist-worn physiological recordings acquired during cognitive stress and relaxation tasks. Adaptive and robust control architectures were designed for closed-loop regulation of latent arousal states. <italic>Results:</i> Simulations based on experimental data showed that both controllers outperformed static methods. On average, under inhibitory and excitatory conditions, the adaptive controller achieved average RMSE reductions of 26.9% and 51.6%, respectively, while the robust controller achieved reductions of 16.0% and 23.4%. In complex multi-step tracking, the adaptive controller reduced average RMSE by 33.7% and control effort by 18.5%; similarly, the robust controller reduced RMSE by 32.6% and control effort by 15.1%. <italic>Conclusion:</i> These findings demonstrate that adaptive and robust control strategies can reliably manage dynamic arousal regulation, offering potential for real-world neuroadaptive systems supporting human performance and well-being.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"128-138"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665497","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":"Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning","authors":"Krinos Li;Xianglu Xiao;Zijun Zhong;Guang Yang","doi":"10.1109/OJEMB.2026.3667030","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667030","url":null,"abstract":"<italic>Goal:</i> Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candidate ligands. While recent advances have been made in predicting protein-ligand complex structures, existing algorithms for interaction and affinity prediction suffer from a sharp decline in performance when handling ligands bound with novel unseen proteins. <italic>Methods:</i> We propose IPBind, a geometric deep learning-based computational method, enabling robust predictions by leveraging interatomic potential between complex's bound and unbound status. <italic>Results:</i> Experimental results on widely used binding affinity prediction benchmarks demonstrate the effectiveness and universality of IPBind. Meanwhile, it provids atom-level insights into prediction. <italic>Conclusions:</i> This work highlight the advantage of leveraging machine learning interatomic potential for predicting protein-ligand binding affinity.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"86-93"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557675","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":"Quantitative Analysis of the Impact of Region of Interest Information on Deep Learning Algorithms for Thyroid Ultrasound Imaging","authors":"Hyunju Lee;Jin Young Kwak;Eunjung Lee","doi":"10.1109/OJEMB.2026.3667415","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667415","url":null,"abstract":"<italic>Goal:</i> To quantitatively assess the impact of incorporating radiologist-defined Region of Interest (ROI) information in training deep learning models for thyroid ultrasound image classification and lesion localization. Methods: We compared a conventional convolutional neural network (CNN) trained without ROI information, interpreted through Grad-CAM for attention visualization, to Faster R-CNN and YOLOv2 models trained with radiologist-validated ROI masks. We also introduced an adapted mosaic-based composite input, derived from mosaic augmentation but implemented as fixed 1 × 2 and 2 × 2 layouts, to improve class balance and spatial diversity in training. Results: Models trained with ROI guidance achieved higher performance in both localization and classification compared to those trained without ROI. The average classification accuracy increased from about 80% in the baseline CNN to around 85% in ROI-guided models that shows an improvement of approximately 5 percentage points. The mean intersection over union between detected and radiologist-defined ROIs increased from approximately 33% to over 70%. The adapted mosaic input further stabilized performance across epochs and improved sensitivity while maintaining comparable specificity. Conclusions: Incorporating radiologist-defined ROI information and structured mosaic inputs significantly improves both diagnostic accuracy and localization precision. These results demonstrate that integrating ROI-guided learning with context-preserving composite inputs provides a reproducible framework for developing reliable AI systems in thyroid ultrasonography.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"172-179"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828942","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}