IEEE Journal of Biomedical and Health Informatics最新文献

{"title":"In Vitro Diagnosis of Parkinson's Disease Based on Facial Expression and Behavioral Gait Data.","authors":"Yinxuan Xu, Yintao Zhou, Zhengyu Li, Jing Huang, Wei Huang","doi":"10.1109/JBHI.2025.3563902","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563902","url":null,"abstract":"<p><p>Parkinson's disease (PD) is characterized by incurable, rapid progression, and severe disability, severely impacting the lives of patients and their families. With an aging population, the need for early detection of PD is increasing. In vitro diagnosis has attracted attention because of its non-invasiveness and low cost, but there are some problems with the existing methods: 1) facial expression diagnosis has little training data; 2) gait diagnosis requires specialized equipment and acquisition environment, which is poorly generalizable; 3) a single modality is easy to miss the diagnosis; and 4) multimodal diagnostic methods are not universally applicable. To address the above issues, we propose a novel multimodal in vitro diagnostic method for PD based on facial expression and behavioral gait. The method uses a lightweight deep learning model for feature extraction and feature fusion to improve diagnostic accuracy and ease of use. Meanwhile, we have established the largest multimodal PD data set in collaboration with hospitals and conducted a large number of experiments to verify the effectiveness of the method.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001858","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}

{"title":"Watermarking Protocol Inspired Kidney Stone Segmentation in IoMT.","authors":"Parkala Vishnu Bharadwaj Bayari, Nishtha Tomar, Gaurav Bhatnagar, Chiranjoy Chattopadhyay","doi":"10.1109/JBHI.2025.3563955","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563955","url":null,"abstract":"<p><p>The rapid explosion of medical data, exarcebated by the demands of smart healthcare, poses significant challenges for authentication and integrity verification. Moreover, the surge in cybercrime targeting healthcare data jeopardizes patient privacy, compromising both trust and diagnostic reliability. To address these concerns, we propose a robust healthcare system that integrates a kidney stone segmentation framework with a watermarking protocol tailored for Internet of Medical Things (IoMT) applications. Drawing upon patient information and biometrics, chaotic keys are generated for obfuscation and randomization, along with the watermark for integrity verification and authentication. The watermark is imperceptibly embedded into the obfuscated medical image using Singular Value Decomposition (SVD) and adaptive quantization, followed by randomization. Upon reception, successful watermark extraction and verification ensure secure access to unaltered medical data, enabling precise segmentation. To facilitate this, a ResNeXt-50 inspired encoder and attention-guided decoder are introduced within the U-Net architecture to enhance comprehensive feature learning. The effectiveness and practicality of the proposed system have been evaluated through comprehensive experiments on kidney CT scans. Comparative analysis with state-of-the-art techniques highlights its superior performance.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995632","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}

{"title":"An Unsupervised Correlation Learning-based Clustering Model for Multiple Complex Lesions Evaluation.","authors":"Wenfeng Xu, Cong Lai, Zefeng Mo, Cheng Liu, Maoyuan Li, Gansen Zhao, Kewei Xu","doi":"10.1109/JBHI.2025.3563886","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563886","url":null,"abstract":"<p><p>Lesion morphology and quantity evaluation in computer tomography (CT) images are critical for precise disease diagnosis. Most existing methods employ machine learning-based methods to separately evaluate the morphology and quantity of individual lesion, neglecting the synergy between morphological structure and quantitative distribution. This limitation presents challenges when handling multiple complex lesions. This paper proposes an unsupervised correlation learning-based clustering model for evaluating lesion morphology and quantity in scenarios involving multiple complex lesions without predefined specific-logic. Specifically, the model utilizes clinical knowledge and changes in the in- or out-degree of lesion regions to learn their interdependencies, automatically recognizing domain-specific morphological features. These morphological features serve as key representations for morphology estimation and provide essential contextual information for quantity analysis. Furthermore, the model perceives quantity evaluation as a density-based clustering process. By interacting with domain-specific morphological features, the model dynamically adjusts the search objects, followed by designing morphology-special parameter search strategies to autonomously learn spatial relationships between lesion regions. This approach facilitates the exploration of optimal parameters for accurate lesion evaluation without manual intervention. Experiments conducted on the kidney stone dataset including 53 samples and the kidney tumor dataset comprising 300 samples, indicate that the proposed model has achieved 92.45% and 95.33% accuracy in morphology analysis, respectively. For quantity analysis, the proposed model has achieved 79.25% and 94.33% accuracy, outperforming the well-performing AR-DBSCAN method by +30.19% and DRL-DBSCAN method by +6%. The proposed model is demonstrated to be effective in handling morphology and quantity estimation for multiple complex lesions.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965542","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}

{"title":"MuFuBP-Net: A Multimodal Fusion Network for Cuffless Blood Pressure Estimation Using Dual-Feature Pipeline with Probabilistic Feature Encoder.","authors":"Farhad Hassan, Mubashir Ali, Zubair Akbar, Jingzhen Li, Yuhang Liu, Weihao Wang, Lixin Guo, Zedong Nie","doi":"10.1109/JBHI.2025.3563852","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563852","url":null,"abstract":"<p><p>Cuffless blood pressure (BP) estimation is critical for managing growing concerns about hypertension and cardiovascular diseases. Despite recent advancements in multimodal (ECG and PPG) BP estimation methods, which have achieved varying degrees of success, several challenges remain to be addressed. These include capturing the full spectrum of BPrelevant information, redundant feature spaces, and handling the multigrade classification. To address these issues, we propose a Multimodal Fusion BP Network (MuFuBP-Net), featuring a novel dual-feature pipeline architecture designed to extract hierarchical and modality-specific features from both ECG and PPG signals. Additionally, the Cascading Cross-Feature Enhancer (CCFE) module integrates multiple fusion strategies with a squeeze-and-excitation mechanism to apply channel-wise attention to spatial features, enabling dynamic re-weighting. We also employed a Sequence Context Network (SCN) module to capture global sequential features. Subsequently, a Probabilistic Feature Encoder (PFE) encodes the multilevel features from both pipelines into a compact latent space, preserving their discriminative characteristics. Our approach achieved MAE ± SDE of 2.99 ± 4.37 mmHg (SBP) and 2.63 ± 4.19 mmHg (DBP) on MIMIC-II, and 2.27 ± 4.15 mmHg (SBP) and 1.63 ± 2.96 mmHg (DBP) on MIMIC-III dataset, meeting AAMI, BHS, and IEEE grade A standards. The proposed approach demonstrated competitive results compared to existing techniques, highlighting its significance as a reliable solution for cuffless BP monitoring.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019927","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}

{"title":"A Multi-IMU System for Assessing Human Walking Dynamics Balance Using the Compass Gait Model.","authors":"Bingfei Fan, Luobin Zhang, Zhiheng Wang, Mingyu Du, Shibo Cai, Tianyu Jiang","doi":"10.1109/JBHI.2025.3563479","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563479","url":null,"abstract":"<p><p>Assessing walking balance is crucial for identifying fall risks in older adults and optimizing rehabilitation strategies for patients with walking impairments. However, current laboratory-based methods for assessing dynamic walking balance are hard to use in daily life scenarios. To address this issue, we proposed a walking balance analysis method based on a self-developed inertial measurement unit (IMU) system consisting of 17 low-cost IMUs. This method collects motion data from key segments of the human body using the IMUs, then uses OpenSim to reconstruct human motion and extract gait parameters, and finally, analyzes walking stability through an improved compass gait model. For validation, we recruited 20 subjects to perform normal and perturbed walking experiments, and the optical motion capture system was used as the reference system. Results indicated that the root mean square error (RMSE) of the gait cycle was 0.158 seconds, and RMSEs of step length and maximum foot clearance were 0.025 m and 0.045 m, respectively. Under normal walking conditions, we calculated the balance indicator, the minimum Euclidean distance, and its RMSE was 0.027. In the perturbed walking experiment, we found that the state point significantly exceeded the balance boundary, then gradually converged and returned to the steady state, showing the effectiveness of the proposed balance stability assessment method. The developed system and the proposed method have the advantages of lightweight design, flexible application scenarios, and low power consumption, which provide a novel technical approach for daily monitoring and assessing walking balance in patients with walking impairments.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009819","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}

{"title":"When East Meets West: Cross-domain Drug Interaction Annotations with Large Language Models and Bidirectional Neural Networks.","authors":"Ruoxuan Zhang, Weidun Xie, Qiuzhen Lin, Xiangtao Li, Ka-Chun Wong","doi":"10.1109/JBHI.2025.3563289","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563289","url":null,"abstract":"<p><p>Drug combination therapy is a promising strategy for managing complex and co-existing diseases. However, drug-drug interactions (DDIs) can result in unexpected adverse effects, making it crucial to understand such interactions to prevent adverse drug reactions and develop new therapeutic strategies. Current DDI annotation methods heavily rely on atom-level graph structural features, overlooking valuable drug contextual representations within retrieval from medical resources. Additionally, these methods are typically designed for a specific task, limiting their scalability to broader medical scenarios. To address these limitations, we propose TEmbed-DDI, a novel framework that leverages meaningful contextual representations and pre-trained large language model embeddings to enhance feature extraction for DDI annotations. Specifically, we retrieve meaningful contextual texts for each drug to enrich semantic features and use pre-trained large language model embeddings to capture rich features from these long-range contextual representations. TEmbed-DDI is the first framework to incorporate LLM-powered embeddings for medical interaction annotations. Furthermore, a bidirectional learning neural network is integrated into TEmbed-DDI for the integrative Western and traditional Chinese medicine DDI annotation tasks. Comparative results demonstrate that TEmbed-DDI achieves state-of-the-art performance, with the highest AUC scores of 0.992 and 0.95 on the Western CHCH and DEEP interaction annotation benchmarks. Even when evaluated on the newly constructed Traditional Chinese Medicine (TCM) DDI annotation benchmark, TEmbed-DDI consistently exhibits outstanding generalization capability, achieving an AUC of 0.956. Moreover, case studies further validate TEmbed-DDI's capability to annotate previously unknown interactions. These findings suggest that TEmbed-DDI can serve as a valuable tool in annotating previously unknown drug combinations for real-world applications, facilitating the development of more effective therapies. Furthermore, as the first framework combining traditional Chinese medicine into DDI annotation tasks, its adaptability highlights potential in supporting cross-domain medical research. TEmbed-DDI's design principles can inspire the development of flexible, LLM-powered frameworks for drug discovery and medical research.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963285","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}

{"title":"FRSynergy: A Feature Refinement Network for Synergistic Drug Combination Prediction.","authors":"Lei Li, Haitao Li, Chunhou Zheng, Yansen Su","doi":"10.1109/JBHI.2025.3563433","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3563433","url":null,"abstract":"<p><p>Synergistic drug combinations have shown promising results in treating cancer cell lines by enhancing therapeutic efficacy and minimizing adverse reactions. The effects of a drug vary across cell lines, and cell lines respond differently to various drugs during treatment. Recently, many AI-based techniques have been developed for predicting synergistic drug combinations. However, existing computational models have not addressed this phenomenon, neglecting the refinement of features for the same drug and cell line in different scenarios. In this work, we propose a feature refinement deep learning framework, termed FRSynergy, to identify synergistic drug combinations. It can guide the refinement of drug and cell line features in different scenarios by capturing relationships among diverse drug-drug-cell line triplet features and learning feature contextual information. The heterogeneous graph attention network is employed to acquire topological information-based original features for drugs and cell lines from sampled sub-graphs. Then, the feature refinement network is designed by combining attention mechanism and context information, which can learn context-aware feature representations for each drug and cell line feature in diverse drug-drug-cell line triplet contexts. Extensive experiments affirm the strong performance of FRSynergy in predicting synergistic drug combinations and, more importantly, demonstrate the effectiveness of feature refinement network in synergistic drug combination prediction.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965139","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}

{"title":"Poincaré Image Analysis of Short-Term Electrocardiogram for Detecting Atrial Fibrillation.","authors":"Md Mayenul Islam, Mohammod Abdul Motin, Sumaiya Kabir, Dinesh Kumar","doi":"10.1109/JBHI.2025.3562778","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562778","url":null,"abstract":"<p><p>Atrial fibrillation (AF) is a heart rhythm disorder and is associated with the risk of stroke and heart failure. Early detection of AF is crucial but challenging due to its asymptomatic nature and similarity to other ectopic beats, such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). This article presents a novel Poincaré image-domain feature-based automated AF screening model in the presence of PACs/PVCs using 10-second single-lead electrocardiogram (ECG) signals. The model proposes a hybrid approach that integrates a radial basis function-based support vector machine classifier, optimized via grid search, with a rule-based decision criterion. A set of 84 Poincaré image features is extracted and reduced to a set of four features through the minimum redundancy maximum relevance selection approach and then fed into the classifier. Additionally, rules based on P-wave information and dRR distribution patterns are incorporated to enable a more distinct separation of PACs/PVCs from AF. The model was validated using eight datasets comprising recordings from 25,776 subjects. Both 5-fold cross-validation and leave-one-dataset-out validation were performed using 2,06,367 segments: 1,12,591 normal, 9,485 PACs/PVCs, and 84,291 AF segments. The accuracy ranges were 96.35% to 99.40% and 96.48% to 99.33% for 5-fold cross-validation and leave-one-dataset-out validation, respectively, with balanced sensitivity and specificity across all datasets. The model's superior performance across diverse data demonstrates its robustness and suitability for real-world application, supporting its potential in computerized assessment of short-term ECGs to detect AF.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998256","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}

{"title":"Exploiting Trusted Execution Environments and Distributed Computation for Genomic Association Tests.","authors":"Claudia V Brito, Pedro G Ferreira, Joao T Paulo","doi":"10.1109/JBHI.2025.3562364","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562364","url":null,"abstract":"<p><p>Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing novel biological insights and therapeutic applications. However, analyzing large amounts of sensitive data raises key data privacy concerns, specifically when the information is outsourced to untrusted third-party infrastructures for data storage and processing (e.g., cloud computing). We introduce Gyosa, a secure and privacy-preserving distributed genomic analysis solution. By leveraging trusted execution environments (TEEs), Gyosa allows users to confidentially delegate their GWAS analysis to untrusted infrastructures. Gyosa implements a computation partitioning scheme that reduces the computation done inside the TEEs while safeguarding the users' genomic data privacy. By integrating this security scheme in Glow, Gyosa provides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of Gyosa, reinforcing its ability to provide enhanced security guarantees.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003141","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}

{"title":"Interval-Shared Information Integration and False-Negative Association Reduction in Multi-Source MiRNA-Disease Association Prediction.","authors":"Qinghang Cui, Honglie Guo, Yueyi Cai, Yu Fei, Shunfang Wang","doi":"10.1109/JBHI.2025.3562617","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562617","url":null,"abstract":"<p><p>Numerous studies have demonstrated that microRNAs (miRNAs) play crucial roles in the development and progression of various diseases, making the identification of miRNA-disease association (MDA) essential for understanding human disease etiology. While several computational models have been developed to predict MDAs, challenges persist-particularly the limited consideration of information interactions among multi-source similarities and the presence of \"false-negative\" associations in the original topology. To address these issues, we propose ISFNMDA, a model designed to infer potential MDAs by leveraging multi-view collaborative learning for feature extraction and optimizing association topology through graph structure momentum contrastive learning. Specifically, multi-source similarities of miRNAs and diseases are mapped into a unified feature space via encoders. The Pearson correlation coefficient is employed to derive pairwise constraints between nodes, facilitating information interactions and constructing interval-shared information constraints. Subsequently, an inference graph learner models the representations to generate an inferred graph topology. By maximizing mutual information between the inferred topology and the original \"false-negative\" associations through momentum contrastive learning, the model effectively reduces spurious correlations. The final comprehensive representations and optimized graph structure are then used to predict potential MDAs. Experimental results demonstrate that ISFNMDA outperforms existing methods, and case studies further validate its predictive capability.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970036","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}