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

{"title":"Guest Editorial: Special Section on Conformable Decoders","authors":"Canan Dagdeviren","doi":"10.1109/OJEMB.2025.3555346","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3555346","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"352-352"},"PeriodicalIF":2.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821865","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":"Motion Compensation in Pulmonary Fluorescence Lifetime Imaging: An Image Processing Pipeline for Artefact Reduction and Clinical Precision","authors":"Tarek Haloubi;Spencer Angus Thomas;Catherine Hines;Kevin Dhaliwal;James R. Hopgood","doi":"10.1109/OJEMB.2025.3558620","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3558620","url":null,"abstract":"<italic>Goal:</i> This study introduces Temporal Reliability and Accuracy via Correlation Enhanced Registration (TRACER), a novel image processing pipeline that addresses motion artefacts in real-time Fluorescence Lifetime Imaging (FLIm) data for in-vivo pulmonary Optical Endomicroscopy (OEM). Its primary objective is to improve the accuracy and reliability of FLIm image sequences. <italic>Methods:</i> The proposed TRACER pipeline comprises a comprehensive sequence of pre-processing steps and a novel registration approach. This includes the removal of uninformative frames and motion characterisation through dense optical flow, followed by a tracking-based Normalised Cross Correlation image registration method leveraging Channel and Spatial Reliability Tracker for precise alignment. <italic>Results:</i> The complete TRACER pipeline delivers significant performance improvements, with 20% to 30% enhancement across different metrics for all tested registration methods. In particular, the unique TRACER registration approach outperforms state-of-the-art methods in image registration performance and achieves an order-of-magnitude faster runtime than the next best-performing approach. <italic>Conclusion:</i> By addressing motion artefacts through its integrated pre-processing and novel registration strategy, TRACER offers a robust solution that ensures improved image quality and real-time feasibility for FLIm data processing in <italic>in-vivo</i> pulmonary OEM.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"432-441"},"PeriodicalIF":2.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865263","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":"Endovascular Localization of Aortic Injury in a Porcine Model","authors":"Saaid H. Arshad;Ryan L. Touzjian;Matthew C. Jones;Brian A. Telfer;Jason M. Rall;Theodore G. Hart;Marlin W. Causey","doi":"10.1109/OJEMB.2025.3556987","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3556987","url":null,"abstract":"<italic>Goal</i>: Non-compressible torso hemorrhage represents a category of lethal injuries in both civilian and military traumatically injured populations that with proper intervention, training, or technological advancements are survivable. Endovascular localization of active bleeding in the pre-hospital setting can allow faster, less invasive, and more accurate applications of life-saving interventions. In this paper, we report initial in vivo and in silico experimental results to test the feasibility of endovascular localization of hemorrhage. <italic>Methods:</i> Endovascular pressure waveforms were acquired on five pigs with an induced aortic injury via a custom intra-aortic catheter instrumented with four pressure sensors. Pressure and velocity data were then simulated on an in silico human aortic model with the same kind of injury. <italic>Results:</i> A decrease in pulse pressure across the injury (proximal to distal) reliably indicated the injury location to within a few centimeters. The simulated model showed a similar decrease in pulse pressure as well as an increase in velocity<italic>. Conclusions:</i> With additional refinement, localization accuracy may be sufficient for application of a modern covered stent to stop bleeding. The simulated model results indicate relevance for humans and provide guidance for future experiments.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"425-431"},"PeriodicalIF":2.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865277","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 Method for Temporally Resolved Continuous Inline Measurement of Multiple Solute Concentrations With Microfluidic Spectroscopy","authors":"Andrea Lorenzo Henri Sergio Detry;Vinny Chandran Suja;Nathaniel Merriman Sims;Robert A. Peterfreund;David E. Arney","doi":"10.1109/OJEMB.2025.3555807","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3555807","url":null,"abstract":"<italic>Goal:</i> To develop a compact, real-time microfluidic spectroscopy system capable of simultaneously measuring the concentrations of multiple solutes flowing together through a single fluid pathway with high temporal resolution. <italic>Methods:</i> The measurement system integrates a Z-flow cell and dual-wavelength LED light sources with a compact spectrophotometer. The experimental setup consisted of two clinical infusion pumps delivering distinct marker dyes through a common fluid pathway composed of a clinical manifold and a single lumen of a clinical intravascular catheter, while a third pump delivered an inert carrier fluid. Concentration measurements of the mixed dyes were performed at high-frequency sampling intervals, with dynamic pump rate adjustments to evaluate the system's ability to detect real-time changes in solute concentration. A MATLAB-based control application enabled automated data acquisition, processing, and system control to enhance experimental efficiency. <italic>Results:</i> The system accurately measured solute concentrations, capturing temporal variations with high precision. It demonstrated high reproducibility with a standard error of the mean no larger than <inline-formula><tex-math>$0.19 ,mu mathrm{g}mathrm{/}mathrm{m}mathrm{L}$</tex-math></inline-formula> for Erioglaucine and <inline-formula><tex-math>$1.32 ,mu mathrm{g}mathrm{/}mathrm{m}mathrm{L}$</tex-math></inline-formula> for Tartrazine at steady state, and high accuracy with a maximum deviation of <inline-formula><tex-math>$0.21 ,mu mathrm{g}mathrm{/}mathrm{m}mathrm{L}$</tex-math></inline-formula> for Erioglaucine and <inline-formula><tex-math>$0.5 ,mu mathrm{g}mathrm{/}mathrm{m}mathrm{L}$</tex-math></inline-formula> for Tartrazine from the expected steady-state concentrations. <italic>Conclusions:</i> This system enables continuous, real-time monitoring of multiple solutes in dynamic flow conditions, offering a portable solution with high sensitivity to temporal concentration changes—advancing beyond traditional static fluid measurement methods.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"442-449"},"PeriodicalIF":2.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877629","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":"Cross-Scale Guidance Integration Transformer for Instance Segmentation in Pathology Images","authors":"Yung-Ming Kuo;Jia-Chun Sheng;Chen-Hsuan Lo;You-Jie Wu;Chun-Rong Huang","doi":"10.1109/OJEMB.2025.3555818","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3555818","url":null,"abstract":"<italic>Goal:</i> To assess the degree of adenocarcinoma, pathologists need to manually review pathology images. To reduce their burdens and achieve good inter-observer as well as intra-observer reproducibility, instance segmentation methods can help pathologists quantify shapes of gland cells and provide an automatic solution for computer-assisted grading of adenocarcinoma. However, segmenting individual gland cells of different sizes remains a difficult challenge in computer aided diagnosis. <italic>Method:</i> A novel cross-scale guidance integration transformer is proposed for gland cell instance segmentation. Our network contains a cross-scale guidance integration module to integrate multi-scale features learned from the pathology image. By using the integrated features from different field-of-views, the decoder with mask attention can better segment individual gland cells. <italic>Results:</i> Compared with recent task-specific deep learning methods, our method can achieve state-of-the-art performance in two public gland cell datasets. <italic>Conclusions:</i> By imposing cross-scale encoder information, our method can retrieve accurate gland cell segmentation to assist the pathologists for computer-assisted grading of adenocarcinoma.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"413-419"},"PeriodicalIF":2.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830497","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":"Fetal Health Prediction From Cardiotocography Recordings Using Kolmogorov–Arnold Networks","authors":"W. K. Wong;Filbert H. Juwono;Catur Apriono;Ismi Rosyiana Fitri","doi":"10.1109/OJEMB.2025.3549594","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3549594","url":null,"abstract":"<italic>Goal:</i> Cardiotocograph (CTG) is a widely used device for monitoring fetal health during the labor phase. However, its interpretation remains challenging due to the complex and nonlinear nature of the data. Therefore, this paper aims to propose a reliable machine learning model for predicting fetal health. <italic>Methods:</i> This paper introduces a state-of-the-art approach for predicting fetal health from CTG recordings (statistical features) using the Kolmogorov-Arnold Networks (KANs). KANs have recently been proposed asa powerful competitor to the conventional transfer function approach in feedforward neural networks. The proposed method leverages the powerful capabilities of KANs to model the intricate relationships within the CTG data, leading to improved classification accuracy. We validate our approach on a publicly available CTG dataset, which consists of statistical features of the acquired recordings and labeled fetal health conditions. <italic>Results:</i> The results show that KANs outperform traditional machine learning models, achieving average classification accuracy values of 93.6% and 92.6% for two-class and three-class classification tasks, respectively. <italic>Conclusion:</i> Our results indicate that the KAN model is particularly effective in handling the nonlinearity inherent in CTG recordings, making it a promising tool for enhancing automated fetal health assessment.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"345-351"},"PeriodicalIF":2.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726439","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":"Assessing the Accuracy of Bed-Occupancy With a tina.care Bed Sensor in Hospital Wards and Home Care Settings: A Pilot Study","authors":"Tomáš Kulhánek;Kvetoslava Hošková;Jitka Feberová;Miroslav Malecha","doi":"10.1109/OJEMB.2025.3548838","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3548838","url":null,"abstract":"<italic>Goal:</i> This pilot study aims to assess accuracy in detecting patient presence or absence by using a bed sensor based on mmwave radar technology above the patient bed. <italic>Methods:</i> Patients and healthy volunteers were observed during their presence or absence in a bed in hospital and home location. These observations were compared with data coming from bed sensor monitoring patient presence using tina.care bed sensor ASWA. <italic>Results:</i> A total of 53 different observations were performed during the study period and the bed sensor reached accuracy of 94%, precision of 90%, sensitivity of 99% and specificity of 89% to detect presence or absence of patients in a bed. <italic>Conclusions:</i> The sensor demonstrated strong performance in detecting patient presence in bed, with reasonable specificity and low false negatives. Further research should assess bed-exit and bed-entry events, system's accuracy in a larger cohort, its impact on patient care, and the precision of vital health parameters measured by the sensor in order to compare it with similar studies.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"420-424"},"PeriodicalIF":2.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848875","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":"Editorial Bringing the American Economic Flywheel to a Screeching Halt","authors":"Donald E. Ingber","doi":"10.1109/OJEMB.2025.3549674","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3549674","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"320-321"},"PeriodicalIF":2.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698291","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":"NDL-Net: A Hybrid Deep Learning Framework for Diagnosing Neonatal Respiratory Distress Syndrome From Chest X-Rays","authors":"Malik Muhammad Arslan;Xiaodong Yang;Nan Zhao;Lei Guan;Tao Cui;Daniyal Haider","doi":"10.1109/OJEMB.2025.3548613","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3548613","url":null,"abstract":"<italic>Objective:</i> Neonatal Respiratory Distress Syndrome (NRDS) poses a significant threat to newborn health, necessitating timely and accurate diagnosis. This study introduces NDL-Net, an innovative hybrid deep learning framework designed to diagnose NRDS from chest X-rays (CXR). <italic>Results:</i> The architecture combines MobileNetV3 Large for efficient image processing and ResNet50 for detecting complex patterns essential for NRDS identification. Additionally, a Long Short-Term Memory (LSTM) layer analyzes temporal variations in imaging data, enhancing predictive accuracy. Extensive evaluation on neonatal CXR datasets demonstrated NDL-Net's high diagnostic performance, achieving 98.09% accuracy, 97.45% precision, 98.73% sensitivity, 98.08% F1-score, and 98.73% specificity. The model's low false negative and false positive rates underscore its superior diagnostic capabilities. <italic>Conclusion:</i> NDL-Net represents a significant advancement in medical diagnostics, improving neonatal care through early detection and management of NRDS.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"407-412"},"PeriodicalIF":2.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10914519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821686","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":"Source-Detector Geometry Analysis of Reflective PPG by Measurements and Simulations","authors":"M. Reiser;T. Mueller;A. Breidenassel;O. Amft","doi":"10.1109/OJEMB.2025.3546771","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3546771","url":null,"abstract":"<italic>Goal:</i> We investigate the effect of source-detector geometry, including distance and angle, on the reflective photoplethysmography (PPG) signal. <italic>Methods:</i> A porcine skin phantom was used for laboratory measurements and replicated by Monte Carlo simulations. Variations in sensor geometry were analysed. <italic>Results:</i> Laboratory measurements and Monte Carlo simulations showed agreement for various geometry settings. With decreasing negative sensor angle, the differential path length factor and the average maximum penetration depth increases. <italic>Conclusions:</i> Our analyses highlight the influence of source-detector geometry on the PPG DC signal. Based on our analysis of penetration depth and optical path length, the geometry effects can be transferred to the PPG AC signal too. MC simulations provide an important tool to optimise PPG performance.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"400-406"},"PeriodicalIF":2.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821866","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}