Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献

{"title":"Evaluation of Targeted Robotic Balance Training in Chronic TBI.","authors":"Kiran K Karunakaran, Prasad A Tendolkar, Guang H Yue, Easter S Suviseshamuthu","doi":"10.1109/EMBC58623.2025.11253061","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253061","url":null,"abstract":"<p><p>Traumatic brain injury (TBI) impairs sensorimotor functions, which affect static, dynamic, and reactive balance even in chronic stages. Varying levels of deficits in people with TBI (pwTBI) due to heterogeneous injury pose challenges on therapies. Since qualitative assessment-based conventional therapies are multifactorial, they may not precisely evaluate deficits or provide targeted therapy. However, robotic devices can precisely evaluate deficits and offer customized therapy progression based on deficits. Therefore, the study objective was to investigate the efficacy of a targeted robotic balance training (RBT) in pwTBI using biomechanical and functional outcomes. Data are presented for a small sample of pwTBI who received RBT (TBI-I) and for those who did not (TBI-C). After 10 sessions of training, TBI-I improved in biomechanical (static, dynamic, and reactive balance as well as limits of stability) and functional (community mobility and balance scale) outcomes. These results underscore the preliminary efficacy of RBT in improving balance and postural control in chronic TBI.Clinical Relevance - The data support the efficacy of RBT that can deliver targeted therapy for pwTBI.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fourier Synchrosqueezed Transform for Shear Wave Speed Estimation in Crawling Wave Sonoelastography Approach.","authors":"Joaquin Sanchez, Sebastian Merino, Cristina Orihuela, Benjamin Castaneda, Stefano E Romero","doi":"10.1109/EMBC58623.2025.11252944","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252944","url":null,"abstract":"<p><p>Crawling Wave Sonoelastography (CWS) is a quantitative elastography technique that employs two mechanical actuators to generate an interference pattern within the tissue. Ultrasound imaging is then used to capture the resulting wave fields, and the shear wave speed (SWS) is computed to produce an elastography image. In previous studies, different time-frequency techniques have been employed to estimate the SWS, but some limitations, such as lateral artifacts and blurred SWS maps, were reported. In this paper, a novel approach based on the Fourier Synchrosqueezed Transform (FSST) is presented. To assert the veracity of the results, previous datasets in homogeneous and heterogeneous phantoms with vibration frequencies between 200 and 360 Hz have been used. The proposed metrics for comparison were SWS mean value and standard variation, coefficient of variation (CV), Bias, R₂₀₈₀, and, contrast-to-noise ratio (CNR). The new estimator demonstrates marginally superior performance in SWS mean value (at 340 Hz, inclusion: 5.13±0.01 m/s, background: 3.42±0.02 m/s) CV (at 320 Hz, inclusion: 0.11%, background: 0%) and CNR (at 320 Hz, 104.7 dB), and better performance in Bias (at 320 Hz, inclusion: 0.6%, background: 0.84%) and R₂₀₈₀ (at 320 Hz, 0.5 mm) in comparison with previous time-frequency approaches.Clinical relevance- This investigation presents a new Shear Wave Speed estimator for Crawling Waves Sonoelastography approach, which is able to quantify stiffness tissue with great accuracy showing the potential of real-time time application to allow the characterization of tissue elasticity.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI for Multi-Label Chest X-ray Diagnosis: Layer-wise Grad-CAM with Hierarchical Feature Extraction.","authors":"Kyungjin Kim, Youna Choi, Jongmo Seo","doi":"10.1109/EMBC58623.2025.11252723","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252723","url":null,"abstract":"<p><p>Artificial intelligence (AI) has become indispensable in medical image analysis, with models such as convolutional neural networks (CNNs) and Transformer achieving remarkable success in diagnostic imaging. Despite their impressive performance, these models often lack interpretability, limiting their adoption in clinical workflows where understanding disease-specific features is critical for trust.In this study, we propose an explainability framework that enhances interpretability for multi-label disease classification in chest X-ray (CXR) diagnosis by utilizing the U-Net encoder-decoder architecture. The encoder and decoder outputs are concatenated to effectively capture hierarchical features for the classification of 14 observations in the MIMIC-CXR dataset. To further improve interpretability, we apply gradient-weighted class activation mapping (Grad-CAM) across multiple layers, providing detailed insights into the refinement of hierarchical features and the emphasis on disease-specific regions throughout the network. This integration of U-Net with an explainable AI (XAI) framework enhances transparency in the diagnostic process, supporting more informed and trustworthy clinical decision making.Clinical relevance- This study underscores the importance of interpretability in AI-based radiology. By providing clear Grad-CAM visualizations of disease-specific features, clinicians can more confidently validate model predictions and incorporate these insights into their decision-making processes. Through enhanced transparency, our approach not only improves diagnostic performance, but also fosters greater trust in AI tools, paving the way for these models to serve as robust, clinician-friendly decision support systems in routine radiological workflows.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Predicting Transitions to Compliant Surfaces in Adults with Transtibial Amputation: A Real-Time Classification Approach.","authors":"Charikleia Angelidou, Jaclyn M Sions, Panagiotis Artemiadis","doi":"10.1109/EMBC58623.2025.11251785","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251785","url":null,"abstract":"<p><p>Walking on compliant surfaces, such as carpets, grass, and soil, presents a unique challenge, particularly for those relying on prosthetic interventions. Ensuring the safety, stability, and fluidity of movement on these surfaces is paramount to prevent falls and related balance issues in this population. This study presents the first attempt to classify and predict surface compliance in individuals with transtibial lower-limb amputations. By integrating electromyographic (EMG), kinematic, and kinetic data, our system effectively distinguishes user intent across varying surface stiffnesses representing diverse real-world terrains. As we demonstrate the algorithm's success within a clinical population, we achieve up to 83% prediction accuracy, attaining comparable results as in previously tested healthy populations. The suggested framework is a critical component for high-level controllers for advanced prostheses and it holds potential for real-time integration, enabling adaptive adjustments to the prosthetic device in response to both user intent and environmental stimuli.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Ultrasound Image Segmentation in Data-Scarce Scenarios Using Self-Supervised Learning With Phantom Data Pre-Training.","authors":"Bo Jiang, Keshi He, Hayoung Cho, Michael J Naughton, Bryan J Ranger","doi":"10.1109/EMBC58623.2025.11254552","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254552","url":null,"abstract":"<p><p>Ultrasound image segmentation is often limited by the scarcity of annotated datasets, especially in resource-constrained clinical settings. To address this issue, we employ BT-UNet, a self-supervised learning framework that combines Barlow Twins (BT) with the UNet architecture, and aim to enhance segmentation performance in low-data conditions. Unlike previous work that trains BT-UNet exclusively on clinical datasets, our approach explores the benefits of pre-training BT-UNet on musculoskeletal phantom ultrasound images, before fine-tuning it on a small set of annotated clinical images. Our results demonstrate that this strategy significantly improves segmentation performance under limited annotated data. Specifically, with only 5% of the labeled clinical dataset, BT-UNet achieves a Dice score of 0.9311, slightly outperforming the standard UNet's 0.9250. However, at an extreme data scarcity level of 1%, BT-UNet maintains a Dice score of 0.7114, whereas UNet drops to 0.2253. These results highlight the potential of self-supervised pre-training on phantom datasets to address data scarcity challenges in medical imaging. By utilizing unlabeled phantom data for representation learning, BT-UNet enhances segmentation accuracy with minimal clinical annotations, offering a promising solution for real-world medical applications where annotated data is limited.Clinical relevance: This study shows that pre-training a self-supervised learning model on musculoskeletal phantom ultrasound images and fine-tuning it with limited clinical data can significantly improve segmentation accuracy, offering a promising solution to reduce reliance on large annotated datasets.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How do Body Mass Index (BMI) and Gender Affect Time-Up-and-Go Measurements.","authors":"Diego Rendon, Mario Ibarra, Irene Cheng","doi":"10.1109/EMBC58623.2025.11253415","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253415","url":null,"abstract":"<p><p>Time-Up-and-Go (TUG) is a commonly used clinical test to evaluate an individual's gait and frailty state. By combining TUG data with other knowledge, e.g., nutrition and daily habits, informed decisions can be made to delay the progression of or alleviate chronic diseases, such as Parkinson's. Scheduling TUG tests in clinics requires assisted transportation and appointment. With the increasingly overloaded healthcare system, recent advances in e-Health provide an alternative solution. Research studies suggest that it is feasible to perform tests at home and automate gait analysis using intelligent software to classify frailty levels in a remote setting. This allows more frequent monitoring, and clinical appointments are made only to patients at higher risk or those in need. However, conducting the TUG test at home comes with challenges. In this paper, we discuss these challenges, e.g., cluttered environment, and propose solutions. In addition, we investigate whether Body Mass Index (BMI) and gender can affect gait measurement. Our experimental results demonstrate that some machine learning models perform better and the choice of input parameters plays an important role in the classification accuracy. Our experimental results demonstrate that high BMI can be reflected in an individual's TUG, if a robust machine learning model is deployed, while men and women in general show distinct gait measurements. Based on this finding, different thresholds should be defined when making the frail, pre-frail and healthy assessment.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully automated gait analysis with earables: Evaluation of an End2End pipeline with hearing-aid integrated accelerometers.","authors":"Ann-Kristin Seifer, Lukas Jahnel, Arne Kuderle, Ronny Hannemann, Bjoern M Eskofier","doi":"10.1109/EMBC58623.2025.11254014","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254014","url":null,"abstract":"<p><p>Earables, due to their unobtrusive and lightweight nature, are increasingly being recognized for their potential in estimating digital biomarkers, yet their application in gait analysis (GA) remains limited because comprehensive analytic tools are missing. Existing ear-worn systems have primarily addressed isolated aspects such as gait classification, stride time, or step length estimation, lacking a full end-to-end pipeline. Such pipelines are essential for efficient and automated workflows and real-world applications. This work presents a complete end-to-end GA pipeline for ear-worn accelerometers incorporating multiple algorithms to process raw sensor signals into spatio-temporal parameters. This multi-step approach includes gait sequence detection, event identification, and parameter estimation. We introduce a novel gait sequence detector (GSD) that automatically detects regions of interest in continuous recordings. The integrated spatio-temporal algorithms have already been validated in an isolated setting as part of a previous evaluation study. Using a dataset with three walking speeds and foot-worn IMUs as references, the GSD effectively detects 91 % of gait sequences. The pipeline achieves stride time and SL errors of around 4 % and a gait velocity error of 5.7 %, consistent with prior evaluation for the individual isolated steps. To our knowledge, this is the first end-to-end GA pipeline for earables. Furthermore, the pipeline was released as open-source toolbox (https://github.com/mad-lab-fau/eargait), to facilitate research access and reusability. Our work lays the foundation for automated, continuous, and long-term mobility assessment in home environments using lightweight, unobtrusive earables.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedFMIT: A Foundation Model-Driven Multimodal Fusion Method for Image-Text Disease Diagnosis.","authors":"Shuyu Liang, Junhu Fu, Wutong Li, Chen Ma, Yuanyuan Wang, Yi Guo","doi":"10.1109/EMBC58623.2025.11252735","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252735","url":null,"abstract":"<p><p>Image-text multimodal disease diagnostic models have the potential to provide more precise diagnosis results compared to conventional image-only diagnostic models. Existing image-text multimodal diagnostic methods struggle to address the significant image-text distribution differences and realize comprehensive information interaction between the two modalities. To tackle these issues, we propose a novel foundation model-driven multimodal fusion model, MedFMIT, for image-text disease diagnosis. MedFMIT utilizes the DCA encoders to extract informative and well-aligned visual and textual feature representations, effectively reducing the distributional gap between images and texts while ensuring robust feature extraction. The DMII module is introduced to facilitate comprehensive information interaction between image and text features at both coarse-grained and fine-grained levels. For performance evaluation, we conducted experiments on two multimodal medical classification datasets (image + text) containing computed tomography images and endoscopic optical images. MedFMIT outperforms other state-of-the-art multimodal algorithms, achieving the AUC scores of 92.7% and 85.4% on the two datasets respectively, demonstrating its strong potential for precise medical diagnosis.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors Affecting Muscle Coactivation of Athletes: A Preliminary Study of Professional Rock Climbers.","authors":"Yufeng Zheng, Pingao Huang, Yanjuan Geng, Peng Fang, Hui Wang","doi":"10.1109/EMBC58623.2025.11253650","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253650","url":null,"abstract":"<p><p>The simultaneous activation of agonist and antagonist muscles during limb movement is known as muscle coactivation. The characteristics of muscle coactivation are closely related to the athletic performance of sports athletes, and they have significant implications for guiding athletes in scientific training, reducing muscle fatigue, and preventing sports injuries. The purpose of this study is to investigate the factors that influence muscle coactivation characteristics during elbow joint movements of the upper limbs. We recruited seven professional rock climbers as subjects and used an isokinetic dynamometer along with surface electromyography (sEMG) technology to record the surface EMG of the biceps brachii and triceps brachii during different levels of isokinetic and isotonic movements of the elbow joint. We then analyzed and processed the sEMG signals to calculate the muscle coactivation index for elbow extension and flexion movements. Additionally, we employed a multifactorial repeated measures analysis of variance (ANOVA) to explore the effects of joint movement type, muscle contraction level, and limb laterality. The muscle coactivation index in flexion (40.8% ± 6.3%) was significantly higher than in extension (35.9% ± 12.6%). The results indicated that only joint movement type had a significant main effect on the coactivation index, while the other factors did not demonstrate a significant effect. As rock climbing is a sport that requires balance between the left and right limbs, the coactivation characteristics of the dominant and non-dominant upper limbs show no laterality. This finding suggests that different types of joint movements substantially influence coactivation levels between the biceps brachii and triceps brachii during isokinetic contraction, thereby modulating the synergistic actions of these muscles. This study preliminarily explores the influencing factors of muscle coactivation characteristics in athletes, providing valuable guidance for scientific training.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GenAU-net: Genomic Attention U-net for Lower-Grade Glioma MRI Segmentation.","authors":"Jihoo Chung, Jang-Hwan Choi","doi":"10.1109/EMBC58623.2025.11253150","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253150","url":null,"abstract":"<p><p>Medical image segmentation is pivotal for diagnosing and analyzing brain tumors, particularly lower-grade gliomas (LGG). Accurate tumor delineation is critical for clinical decision-making and treatment planning, yet this task remains challenging due to the complex structure of brain tissues and the heterogeneity of tumor characteristics. In this paper, we propose Genomic Attention U-Net (GenAU-net), an enhanced segmentation framework that integrates genomic clustering data into the widely used Attention U-Net architecture. By incorporating patient-specific genomic information, GenAU-net achieves a more personalized approach to LGG MRI segmentation, demonstrating a DICE score of 0.827 on a public LGG dataset. Leveraging genomic data not only improves segmentation performance but also opens avenues for an individualized diagnosis and treatment strategy.Clinical relevance-This research underscores the potential of incorporating genomic information for more accurate LGG segmentation in brain MRI. By providing richer context in the segmentation process, GenAU-net could help clinicians better identify tumor boundaries, optimize surgical resection or radiation therapy plans, and ultimately guide tailored patient care, improving outcomes and survival rates.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}