IEEE Journal of Biomedical and Health Informatics最新文献_第2页

A Lightweight 3D Distillation Volumetric Transformer for 3D MRI Super-Resolution.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-31 DOI: 10.1109/JBHI.2025.3555603

Jianwei Zhao, Tao Hong, Hao Qi, Zhenghua Zhou, Hai Wang

{"title":"A Lightweight 3D Distillation Volumetric Transformer for 3D MRI Super-Resolution.","authors":"Jianwei Zhao, Tao Hong, Hao Qi, Zhenghua Zhou, Hai Wang","doi":"10.1109/JBHI.2025.3555603","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555603","url":null,"abstract":"Although existing 3D super-resolution methods for magnetic resonance imaging (MRI) volumetric data can provide better visual images than some traditional 2D methods, they should face challenge of increasing network's parameters and computing cost for getting higher reconstruction accuracy. To address this issue, a lightweight 3D multi scale distillation volumetric Transformer, named Transformer-based dual-attention feature distillation (TDAFD) network, is proposed for 3D MRI by utilizing 3D information hiding in images sufficiently. Our TDAFD network contains several proposed dual-attention feature distillation (DAFD) modules and two designed recursive volumetric Transformers (RVT). Concretely, the proposed DAFD module contains a multi-scale feature distillation (MSFD) block for extracting global features under different scales and a feature enhancement dual attention block (FEDAB) for concentrating on the key features better. In addition, our RVT develops 2D Transformer to 3D and save network's parameters via recursion operations for capturing long-term dependencies in volumetric images effectively. Therefore, our proposed TDAFD network can not only extract deeper features via multi scale feature distillation and Transformer, but also realize the balance of performances and network's parameters. Extensive experiments illustrate that our proposed method achieves superior reconstruction performances than some popular 3D MRI SR methods, and saves number of weights and FLOPs.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Flexible Spatio-Temporal Architecture Design for Artifact Removal in EEG with Arbitrary Channel-Settings.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-28 DOI: 10.1109/JBHI.2025.3555813

Yilin Han, Aiping Liu, Heng Cui, Xun Chen

{"title":"A Flexible Spatio-Temporal Architecture Design for Artifact Removal in EEG with Arbitrary Channel-Settings.","authors":"Yilin Han, Aiping Liu, Heng Cui, Xun Chen","doi":"10.1109/JBHI.2025.3555813","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555813","url":null,"abstract":"Electroencephalography (EEG) data is easily contaminated by various sources, significantly affecting subsequent analyses in neuroscience and clinical applications. Therefore, effective artifact removal is a key step in EEG preprocessing. While current deep learning methods have demonstrated notable efficacy in EEG denoising, single-channel approaches primarily focus on temporal features and neglect inter-channel correlations. Meanwhile, multi-channel methods mainly prioritize spatial features but often overlook the unique temporal dependencies of individual channels. A common limitation of both single-channel and multi-channel methods is their strict requirements on the input channel setting, which restricts their practical applicability. To address these issues, we design a flexible architecture named Artifact removal Spatio-Temporal Integration Network (ASTI-Net), a dual-branch denoising model capable of handling arbitrary EEG channel settings. ASTI-Net utilizes spatio-temporal attention weighting with dual branches that capture inter-channel spatial characteristics and intra-channel temporal dependencies. Its architecture incorporates deformable convolutional operations and channel-wise temporal processing, accommodating varying numbers of EEG channels and enhancing applicability across diverse clinical and research settings. By integrating features from both branches through a fusion reconstruction module, ASTI-Net effectively restores clean multi-channel EEG. Extensive evaluation on two semi-simulated datasets, along with qualitative assessment on real task-state EEG data, validates that ASTI-Net outperforms existing artifact removal methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Scale Dynamic Sparse Attention UNet for Medical Image Segmentation.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-28 DOI: 10.1109/JBHI.2025.3555805

Xiang Li, Chong Fu, Qun Wang, Wenchao Zhang, Chen Ye, Junxin Chen, Chui-Wing Sham

{"title":"Multi-Scale Dynamic Sparse Attention UNet for Medical Image Segmentation.","authors":"Xiang Li, Chong Fu, Qun Wang, Wenchao Zhang, Chen Ye, Junxin Chen, Chui-Wing Sham","doi":"10.1109/JBHI.2025.3555805","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555805","url":null,"abstract":"Transformers have recently gained significant attention in medical image segmentation due to their ability to capture long-range dependencies. However, the presence of excessive background noise in large regions of medical images introduces distractions and increases the computational burden on the fine-grained self-attention (SA) mechanism, which is a key component of the transformer model. Meanwhile, preserving fine-grained details is essential for accurately segmenting complex, blurred medical images with diverse shapes and sizes. Thus, we propose a novel Multi-scale Dynamic Sparse Attention (MDSA) module, which flexibly reduces computational costs while maintaining multi-scale fine-grained interactions with content awareness. Specifically, multi-scale aggregation is first applied to the feature maps to enrich the diversity of interaction information. Then, for each query, irrelevant key-value pairs are filtered out at a coarse-grained level. Finally, fine-grained SA is performed on the remaining key-value pairs. In addition, we design an enhanced downsampling merging (EDM) module and an enhanced upsampling fusion (EUF) module for building pyramid architectures. Using MDSA to construct the basic blocks, combined with EDMs and EUFs, we develop a UNet-like model named MDSA-UNet. Since MDSA-UNet dynamically processes only a small subset of relevant fine-grained features, it achieves strong segmentation performance with high computational efficiency. Extensive experiments on four datasets spanning three different types demonstrate that our MDSA-UNet, without using pre-training, significantly outperforms other non-pretrained methods and even competes with pre-trained models, achieving Dice scores of 82.10% on DDTI, 80.20% on TN3K, 90.75% on ISIC2018, and 91.05% on ACDC. Meanwhile, our model maintains lower complexity, with only 6.65 M parameters and 4.54 G FLOPs at a resolution of 224×224, ensuring both effectiveness and efficiency. Code is available at https://github.com/NEU-LX/MDSA-UNet.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Develop a Deep-Learning Model to Predict Cancer Immunotherapy Response Using In-Born Genomes.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-28 DOI: 10.1109/JBHI.2025.3555596

Kai Yan, Zhiheng Zhou, Sihao Liu, Guanghui Wang, Guiying Yan, Edwin Wang

{"title":"Develop a Deep-Learning Model to Predict Cancer Immunotherapy Response Using In-Born Genomes.","authors":"Kai Yan, Zhiheng Zhou, Sihao Liu, Guanghui Wang, Guiying Yan, Edwin Wang","doi":"10.1109/JBHI.2025.3555596","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555596","url":null,"abstract":"The emergence of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, only 15-30% of the cancer patients respond to ICI treatment, which stimulates and enhances host immunity to eliminate tumor cells. ICI treatment is very expensive and has potential adverse reactions; therefore, it is crucial to develop a method which enables to accurately and rapidly assess a patient's suitability before ICI treatment. We complied germline whole-genome sequencing (WES) data of 37 melanoma patients who have been treated with ICIs and sequenced in our lab previously, and the WES data of other 700 ICI-treated cancer patients in public domain. Using these data, we proposed a novel double-channel attention neural network (DANN) model to predict cancer ICI-response and validate the predictions. DANN achieved a mean accuracy and AUC of 0.95 and 0.98, respectively, which outperformed traditional machine learning methods. Enrichment analysis of the DANN-identified genes indicated that cancer patients whose in-born genomic variants might mainly affect host immune system in a wide-ranging manner, and then affect ICI response. Finally, we found a set of 12 genes bearing genomic variants were significantly associated with cancer patient survivals after ICI treatment.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Transformer Network for Predicting Joint Angles and Torques From Multi-Channel EMG Signals in the Lower Limbs. 利用多通道下肢肌电信号预测关节角度和扭矩的双变压器网络

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-27 DOI: 10.1109/JBHI.2025.3555255

Zhuo Wang, Chunjie Chen, Hui Chen, Yizhe Zhou, Xiangyang Wang, Xinyu Wu

{"title":"Dual Transformer Network for Predicting Joint Angles and Torques From Multi-Channel EMG Signals in the Lower Limbs.","authors":"Zhuo Wang, Chunjie Chen, Hui Chen, Yizhe Zhou, Xiangyang Wang, Xinyu Wu","doi":"10.1109/JBHI.2025.3555255","DOIUrl":"10.1109/JBHI.2025.3555255","url":null,"abstract":"Accurate estimation of lower limb joint kinematics and kinetics using wearable sensors enables biomechanical analysis beyond laboratory settings and facilitates real-time adaptation of exoskeleton assistance profiles. This study introduces a Dual Transformer Network (DTN) designed to concurrently estimate multiple joint angles and moments from multi-channel surface electromyography (sEMG) signals in the lower limbs. The performance evaluation of the predicted joint angles for the hip, knee, and ankle showed average root mean square error (RMSE) values of 1.1827, 1.4312, and 0.8113, Pearson correlation coefficients () of 0.9992, 0.9993, and 0.9991, and coefficients of determination () of 0.9847, 0.9858, and 0.9838, respectively. For the predicted joint moments, the corresponding values were RMSE of 0.0458, 0.0341, and 0.0522 Nm/kg, of 0.9978, 0.9972, and 0.9990, and of 0.9825, 0.9801, and 0.9902. Angular velocities, derived by differentiating the estimated joint angles, achieved an RMSE below 0.6530 rd/s, exceeding 0.9534, and above 0.9552. Additionally, joint power, computed as the dot product of predicted joint moments and angular velocities, resulted in RMSE below 0.3823W/kg, above 0.9771, and above 0.8925. These results demonstrate the effectiveness of the proposed network in continuously estimating lower limb kinematics and kinetics, contributing to advancements in assist-as-needed exoskeleton control strategies.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-IID Medical Image Segmentation Based on Cascaded Diffusion Model for Diverse Multi- Center Scenarios.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-27 DOI: 10.1109/JBHI.2025.3549029

Hanwen Zhang, Mingzhi Chen, Yuxi Liu, Guibo Luo, Yuesheng Zhu

{"title":"Non-IID Medical Image Segmentation Based on Cascaded Diffusion Model for Diverse Multi- Center Scenarios.","authors":"Hanwen Zhang, Mingzhi Chen, Yuxi Liu, Guibo Luo, Yuesheng Zhu","doi":"10.1109/JBHI.2025.3549029","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3549029","url":null,"abstract":"Learning from multi-center medical datasets to obtain a high-performance global model is challenging due to the privacy protection and data heterogeneity in healthcare systems. Current federated learning approaches are not efficient enough to learn Non-Independent and Identically Distributed (Non-IID) data and require high communication costs. In this work, a practical privacy computing framework is proposed to train a Non-IID medical image segmentation model under various multi-center setting in low communication cost. Specifically, an efficient cascaded diffusion model is trained to generate image-mask pairs that have similar distribution to the training data of clients, providing rich labeled data on client side to mitigate heterogeneity. Also, a label construction module is developed to improve the quality of generated image-mask pairs. Moreover, a set of aggregation methods is proposed to achieve global model from data generated from Cascaded Diffusion model for diverse scenarios: CD-Syn, CD-Ens and its extension CD-KD. CD-Syn is a one-shot method that trains segmentation model solely on public generated datasets while CD-Ens and CD-KD maximize the utilization of local original data by an extra communication round of ensemble or knowledge distillation. In this way, the setting of our proposed framework is highly practical, providing multiple aggregation methods which can flexibly adapt to varying demands for efficiency, privacy, and accuracy. We systematically evaluated the effectiveness of our proposed framework on five Non-IID medical datasets and observe 5.38% improvement in Dice score compared with baseline method (FednnU-Net) on average.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Camera-Based Dual-Wavelength Defocused Speckle Imaging for Multi-Point Seismocardiographic Motion Measurement.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-27 DOI: 10.1109/JBHI.2025.3555218

Lin Liu, Qiang Wei, Haozhe Li, Caifeng Shan, Wenjin Wang

{"title":"Camera-Based Dual-Wavelength Defocused Speckle Imaging for Multi-Point Seismocardiographic Motion Measurement.","authors":"Lin Liu, Qiang Wei, Haozhe Li, Caifeng Shan, Wenjin Wang","doi":"10.1109/JBHI.2025.3555218","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555218","url":null,"abstract":"Continuous monitoring of cardiac activity is crucial for detecting anomalies such as heart failure and coronary artery disease, and it can alleviate the burden of cardiovascular disease on healthcare systems. This study introduces a novel concept for contactless monitoring of multi-point cardiac motion using dual- wavelength defocused speckle imaging (DW-DSI). A prototype system was developed to measure multi-point seismocardiography (MP-SCG) signals from the atrial and ventricular regions. In addition, blood pressure (BP) monitoring was demonstrated as a proof of concept using the time delay between atrial and ventricular motion signals. An experiment involving 19 subjects with ice water stimulation protocol demonstrated that the performance of BP estimation using time delay features of MP-SCG is comparable to BP estimated from ECG-PPG derived pulse arrival time. The results showed that the best performance was achieved using the correlation features extracted from MP-SCG, such as time delay information and heart rate, in combination with an artificial neural network model. The mean absolute error for systolic/diastolic/mean BP are 6.954 mmHg, 5.368 mmHg and 5.415 mmHg, with Pearson correlation coefficient of 0.639, 0.559 and 0.517. This demonstrates the potential of the camera-based DW-DSI system for measuring MP-SCG and the feasibility towards continuous BP monitoring.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3553789

Carmen Plaza-Seco, Mohammad Baksh, Kenneth E Barner, Manuel Blanco-Velasco

{"title":"DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.","authors":"Carmen Plaza-Seco, Mohammad Baksh, Kenneth E Barner, Manuel Blanco-Velasco","doi":"10.1109/JBHI.2025.3553789","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3553789","url":null,"abstract":"The development of non-invasive markers for assessing the risk of sudden cardiac death has gained significant attention, particularly T-wave alternans (TWA), which can be recorded from surface electrocardiogram (ECG) signals. However, the clinical application of TWA remains insufficiently standardized, complicating its detection in real-world ambulatory environments due to variable conditions that often affect ECG recordings, including dynamic changes, noise, and artifacts. This study presents a Deep Learning (DL) approach designed to detect TWA directly from ECG signals, using transfer learning with robust architectures such as VGG, ResNet, and Inception. Our method simplifies the detection pipeline by eliminating the need for prior signal processing steps such as R-peak identification, T-wave segmentation, or feature engineering. Our models are trained on a custom long-term dataset of real patients, capturing TWA episodes ranging from non-visible micro-alternans to higher amplitude TWA of 20 to 100 V, and incorporating a robust methodology that emphasizes patient separation during training and testing to enhance generalizability. The results demonstrate that our model achieves an F1-score of during ambulatory analysis, outperforming traditional Machine Learning approaches. By eliminating the need for extensive preprocessing, our approach not only enhances the adaptability of TWA detection but also brings the model closer to practical applicability in clinical settings, leading to more efficient and effective risk stratification for sudden cardiac death.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3554364

Fatemeh Salehi, Sara Bayat, Georg Schett, Arnd Kleyer, Thomas Altstidl, Bjoern M Eskofier

{"title":"ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis.","authors":"Fatemeh Salehi, Sara Bayat, Georg Schett, Arnd Kleyer, Thomas Altstidl, Bjoern M Eskofier","doi":"10.1109/JBHI.2025.3554364","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554364","url":null,"abstract":"Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affecting peripheral joints. Before clinical diagnosis, individuals may possess certain antibodies and experience discomfort but without specific signs of RA or inflamed joints. This stage is termed \"preclinical RA,\" as these individuals are at risk of developing the disease. This early stage is difficult to define, necessitating the development of individual risk models. This study aims to estimate the time and risk of RA onset using various survival machine learning models. After identifying the best model, we stratify patients into risk categories and identify key risk factors. Data from 154 anonymized preclinical RA patients were collected and analyzed. Several survival analysis models were evaluated, including Survival Tree, Random Survival Forest, Extreme Gradient Boosting Survival, Linear Multi-Task Model, Neural Multi-Task Model, Support Vector Machines, and Cox Proportional Hazards. The Random Survival Forest model outperformed the others, achieving a mean C-index of 0.798. Using this model, patients were stratified into low-, medium-, and high-risk groups, facilitating personalized scheduling of clinical visits based on RA risk. To enhance model interpretability, SHapley Additive Explanations (SHAP) are employed to identify key risk factors. The baseline level of rheumatoid factor (RF) antibodies is the most significant predictor. Higher levels of anti-cyclic citrullinated peptide (anti-CCP) and RF antibodies at baseline are linked to earlier RA onset. This method provides valuable insights into key factors that might be overlooked in clinical practice and can improve patient management and quality of life for those at risk of developing RA.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBBo-RPSLD: Training a Multimodal BlenderBot for Rehabilitation in Post-Stroke Language Disorder. MBBo-RPSLD：为中风后语言障碍康复训练多模态混合机器人。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3554331

Yangyang Guo, Airu Huang, Bo Peng, Yufeng Li, Wei Gu

{"title":"MBBo-RPSLD: Training a Multimodal BlenderBot for Rehabilitation in Post-Stroke Language Disorder.","authors":"Yangyang Guo, Airu Huang, Bo Peng, Yufeng Li, Wei Gu","doi":"10.1109/JBHI.2025.3554331","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554331","url":null,"abstract":"Stroke, a severe cerebrovascular event, can lead to motor deficits and often impairs language, affecting quality of life. Thus, developing effective rehabilitation models is crucial for enhancing language function and well-being in stroke patients. This paper presents the Multi-Blender model, designed to address the challenges of multimodal data processing and the complexity of medical dialogue in stroke language rehabilitation. The model integrates the multimodal encoding capabilities of ImageBind-LLM with the conversational generation strengths of BlenderBot, creating a tailored rehabilitation solution for stroke patients. We evaluated the model using a range of datasets, including the NINDS dataset, MSDM database, and clinical data from hospitals, focusing on audio-video recognition and speech translation tasks. Our results demonstrate that the Multi-Blender model outperforms existing models, achieving a BLEU score of 30.2 in the AST task, surpassing Whisper Large-v2 and AudioPaLM. In the ASR task, it also displayed superior performance. The model's effectiveness was further validated through an adjusted MME benchmark, where it scored 85.25% in perceptual tasks and 76.83% in cognitive tasks, outperforming other models in language understanding and fluency scoring. These findings indicate that the Multi-Blender model significantly enhances stroke language rehabilitation by improving multimodal data processing and providing accurate, reliable solutions. Future work will focus on expanding the training dataset and optimizing the model to further advance the effectiveness of stroke rehabilitation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0