IEEE Transactions on Biomedical Engineering最新文献

{"title":"Evaluating Trunk Control Ability in Patients with Spinal Cord Injury via a Robotic Brace.","authors":"Xingzhao Guo, Zhihao Zhou, Jiehong Shi, Rongli Wang, Ninghua Wang, Qining Wang","doi":"10.1109/TBME.2025.3565790","DOIUrl":"https://doi.org/10.1109/TBME.2025.3565790","url":null,"abstract":"<p><p>Evaluating trunk control ability is significant in guiding patients towards proper functional training. Existing assessment techniques are subjective with low resolution, lack multi-dimensional assessment capability, or fail to provide active protection for patients during evaluation. This study proposes a robotic brace, RoboBDsys-II, to assess trunk control ability in multiple dimensions. It is capable of implementing external interventions and providing active protection during evaluation, and collecting kinematic, kinetic, and center of pressure data simultaneously. This reduces the evaluation time and allows for simultaneous evaluation. We recruited 10 patients with spinal cord injury, and assessed their trunk muscle strength, range of motion and resistance ability. The relative and absolute reliability were analyzed by ICC and %SEM, and the validity was examined by the correlation metrics. Results indicate heterogeneity of trunk control abilities for patients with spinal cord injury in different directions, guiding therapists to focus on the areas where the patient's abilities are weak. 10/12 metrics in ROM and resistance assessment exhibit good reliability with high ICC (> 0.75) and low %SEM (<30%). A positive correlation between the combined metrics of ROM and MVC and clinical scores is revealed. There is a negative correlation between the metrics of resistance ability and clinical scores. The device demonstrates the potential advantages in evaluating trunk control ability, offering a promising tool for improving patient care and rehabilitation.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968272","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":"Potential of Ultrashort Pulsed Electric Fields to Disrupt Dense Structure in Glioma Tumors.","authors":"Kun Qian, Chenguo Yao, Yancheng Wang, Qiang Yang, Sizhe Xiang, Qiying Pei, Ting Zhu, Hongmei Liu, Shoulong Dong","doi":"10.1109/TBME.2025.3565520","DOIUrl":"https://doi.org/10.1109/TBME.2025.3565520","url":null,"abstract":"<p><p>Two major reasons why chemotherapy and immunotherapy have limited efficacy in treating gliomas are the blood-brain barrier and the dense, solid structure of the glioma. Pulsed electric fields have been a powerful tool for ablating solid tumors, and narrowing pulse duration can improve the field homogeneity penetrating into the tumor. In this study, we used multicellular tumor spheroids (MCTSs) as a model to explore the potential of ultrashort nanosecond pulses to inhibit tumor cell activity while disrupting their dense drug-resistant barriers. Exposure to ultrashort pulsed electric fields can significantly inhibit the viability of U-87 MG, C6, and GL261 spheroids, as indicated by reduced intracellular ATP content. Meanwhile, the proliferative abilities of tumor cells were suppressed, as evidenced by reduced Ki67 protein expression. In addition, it is notable that, after exposure to electric fields, the volume of spheroids increased dramatically. We hypothesize that ultrashort pulsed electric fields can reduce tumor compactness, thereby facilitating drug delivery for immunotherapy and chemotherapy. Immunofluorescence results showed that the cell-cell junction was broken by ultrashort pulsed electric fields with lower expression of adherens junction protein N-cadherin and tight junction protein ZO-1. It is evidenced that the capability of ultrashort pulsed electric fields to downregulate the intercellular adherence, as well as suppress the epithelial-mesenchymal transition, a key process of metastasis of cancer cells. At last, aqueous fluorescent nanoparticles were applied to simulate the anticancer drug or therapeutic antibodies. Under the supervision of fluorescence microscopy, the degree of nanoparticles penetrating into the spheroids was positively related to the number of ultrashort pulsed electric fields, marked with a higher fluorescent signal from the inner quiescent zone or a necrotic core. In conclusion, we emphasize that ultrashort pulsed electric fields could be promising for downgrading the compactness of glioma tumors, being a powerful assisted therapy for the delivery of anticancer drugs and therapeutic antibodies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010706","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":"Conditional Autonomy in Robot-Assisted Transbronchial Interventions.","authors":"Artur Banach, Fumitaro Masaki, Lambros Athanasiou, Franklin King, Hussein Kharroubi, Bassel Tfayli, Hisashi Tsukada, Yolonda Colson, Nobuhiko Hata","doi":"10.1109/TBME.2025.3565915","DOIUrl":"https://doi.org/10.1109/TBME.2025.3565915","url":null,"abstract":"<p><p>Lung cancer is one of the leading causes of cancer-related deaths, and accurate staging is critical for determining the appropriate treatment. Robotic Navigation Bronchoscopy has shown advantages over traditional manual procedures, offering benefits in safety, efficiency, and accessibility. Although there is ongoing discussion regarding autonomous RNB, there is limited focus on the autonomy in advancing the bronchoscope. In this study, we introduce a novel method for conditional autonomy in advancing and aligning a robotic bronchoscope, which was validated in vitro, ex vivo, and in vivo. This conditional autonomy utilizes a monoscopic bronchoscopic view as input, with operators guiding the system by specifying the next airway to enter at branching points. The reachability of target lesions using this conditional autonomy was 73.3% in the phantom study and 77.5% in the ex vivo study. Statistical significance was found in success rates between bifurcations and trifurcations (p = 0.03) and across lobe segments (p = 0.005). The presence of breathing motion did not affect lesion reachability or the success of turns at branching points in the ex vivo studies. In the in vivo study, when comparing conditional automation to humanoperated navigation, the conditional automation took less time to reach the target lesions than human operators. The median time for passing each bifurcation was 2.5 seconds for human operators and 1.3 seconds for conditional automation. By improving precision and consistency in tissue sampling, this technology could redefine the standard of care for lung cancer patients, leading to more accurate diagnoses and therapies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003383","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 Guided Refinement Network Model With Joint Denoising and Segmentation for Low-Dose Coronary CTA Subtle Structure Enhancement.","authors":"Lin Zhao, Shangwen Yang, Zhan Wu, Huazhong Shu, Jean-Louis Coatrieux, Yang Chen","doi":"10.1109/TBME.2025.3561338","DOIUrl":"https://doi.org/10.1109/TBME.2025.3561338","url":null,"abstract":"<p><p>Coronary CT angiography (CCTA) is an essential technique for clinical coronary assessment. However, the risks associated with ionizing radiation cannot be ignored, especially its stochastic effects, which increase the risk of cancer. Although it can effectively alleviate radiation problems, low-dose CCTA can reduce imaging quality and interfere with the diagnosis of the radiologist. Existing deep learning methods based on image restoration suffer from subtle structure degradation after noise suppression, leading to unclear coronary boundaries. Furthermore, in the absence of prior guidance on coronary location, subtle coronary branches will be lost after aggressive noise suppression and are difficult to restore successfully. To address the above issues, this paper proposes a novel Guided Refinement Network (GRN) model based on joint learning for restoring high-quality images from low-dose CCTA. GRN integrates coronary segmentation, which provides coronary location, into the denoising, and the two leverage mutual guidance for effective interaction and collaborative optimization. On the one hand, denoising provides images with lower noise levels for segmentation to assist in generating coronary masks. Furthermore, segmentation provides a prior coronary location for denoising, aiming to preserve and restore subtle coronary branches. GRN achieves noise suppression and subtle structure enhancement for low-dose CCTA imaging through joint denoising and segmentation, while also generating segmentation results with reference value. Quantitative and qualitative results show that GRN outperforms existing methods in noise suppression, subtle structure restoration, and visual perception improvement, and generates coronary masks that can serve as a reference for radiologists to assist in diagnosing coronary disease.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144008634","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 Dual-Purpose Microwave-Optical Component for Wireless Capsule Endoscopy - a Feasibility Study by Radio Link Analysis.","authors":"Pyry Kiviharju, Nam Ha-Van, Lauri Vaha-Savo, Juha Tuomela, Clemens Icheln, Katsuyuki Haneda, Sergei Tretyakov, Jari Holopainen, Qiyin Fang, Hiroaki Hagiwara, Zachary D Taylor","doi":"10.1109/TBME.2025.3564653","DOIUrl":"https://doi.org/10.1109/TBME.2025.3564653","url":null,"abstract":"<p><p>Wireless capsule endoscopy (WCE) is the only option for non-invasive small intestine disease diagnostics. However, the small size of the capsules poses a major design challenge for engineers since multiple optical and electrical components must reside inside the capsules. This article proposes a space-saving dual-purpose component utilized in both optical image capturing and microwave wireless data transmission of a WCE capsule at the 915 MHz industrial-scientific-medical band. The optical performance is verified through ray-tracing simulations, whereas the microwave operation is demonstrated by full-wave impedance simulations and by conducting a radio link simulation and measurement with an up-scaled prototype. The results show that the proposed component can be utilized in capsule optics to achieve an acceptable imaging quality. Furthermore, the component operates as an antenna, providing adequate link gains for WCE wireless communications.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010344","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":"Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation.","authors":"Jiahui Pan, Zhenglang Yang, Qingyu Shen, Man Li, Chunhong Jiang, Yi Li, Yuanqing Li","doi":"10.1109/TBME.2025.3562067","DOIUrl":"https://doi.org/10.1109/TBME.2025.3562067","url":null,"abstract":"<p><p>Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: 1) the limited use of automated spindle detection in ADOC; 2) the difficulty in identifying low-frequency spindles in patient populations; and 3) the lack of effective tools for quantitatively analyzing the relationship between spindle density and patient outcomes. To address these challenges, we propose a novel Deep Learning-Augmented algorithm for automated sleep spindle detection in ADOC patients. This method combines Convolutional Neural Networks with decision tree-assisted validation, using wavelet transform principles to enhance detection accuracy and sensitivity, especially for the slow spindles commonly found in ADOC patients. Our approach not only demonstrates superior performance and reliability but also has the potential to significantly improve diagnostic precision and guide treatment strategies when integrated into clinical practice. Our algorithm was evaluated on the Montreal Archive of Sleep Studies - Session 2 (MASS SS2, n = 19), achieving average F1 scores of 0.798 and 0.841 compared to annotations from two experts. On a self-recorded dataset from ADOC patients (n = 24), it achieved an F1 score of 0.745 compared to expert annotations. Additionally, our analysis using the Spearman correlation coefficient revealed a moderate positive correlation between sleep spindle density and 28-day Glasgow Outcome Scale scores in ADOC patients. This suggests that spindle density could serve as a prognostic marker for predicting clinical outcomes and guiding personalized patient care.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977074","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":"Migration of Deep Learning Models Across Ultrasound Scanners.","authors":"Ufuk Soylu, Varun Chandrasekeran, Gregory J Czarnota, Michael L Oelze","doi":"10.1109/TBME.2025.3564567","DOIUrl":"https://doi.org/10.1109/TBME.2025.3564567","url":null,"abstract":"<p><p>A transfer function approach has recently proven effective for calibrating deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquisition and machine levels. Expanding on this approach, we develop a strategy to acquire the functionality of a DL model from one ultrasound machine and implement it on another in a black-box setting, in the context of QUS. This demonstrates the ease with which the functionality of a DL model can be transferred between machines. While the proposed approach can also assist regulatory bodies in comparing and approving DL models, it also highlights the security risks associated with deploying such models in a commercial scanner for clinical use. The method is a black-box unsupervised domain adaptation technique that integrates the transfer function approach with an iterative schema. It does not utilize any information related to model internals but it solely relies on the availability of an input-output interface. Additionally, we assume the availability of unlabeled data from a testing machine. This scenario could become relevant as companies begin deploying their DL functionalities for clinical use. In the experiments, we used a SonixOne and a Verasonics machine. The model was trained on SonixOne data, and its functionality was then transferred to the Verasonics machine. The proposed method successfully transferred the functionality to the Verasonics machine, achieving a remarkable 98 percent classification accuracy in a binary decision task. This study underscores the need to establish security measures prior to deploying DL models in clinical settings.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970113","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":"Inverse Problem Approach to Aberration Correction for in vivo Transcranial Imaging Based on a Sparse Representation of Contrast-enhanced Ultrasound Data.","authors":"Paul Xing, Antoine Malescot, Eric Martineau, Ravi L Rungta, Jean Provost","doi":"10.1109/TBME.2025.3564473","DOIUrl":"https://doi.org/10.1109/TBME.2025.3564473","url":null,"abstract":"<p><strong>Objective: </strong>Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals.</p><p><strong>Methods: </strong>We propose to use the a priori knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC in vivo in 5 mouse brains.</p><p><strong>Results: </strong>We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 $mu$m to 18.3 $mu$m. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction.</p><p><strong>Conclusion: </strong>We showed that IPAC can perform skull-induced aberration correction and improved Power Doppler as well as ULM images acquired on the mouse brain.</p><p><strong>Significance: </strong>This technique is promising for more reliable transcranial imaging of the brain vasculature with potential non-invasive clinical applications.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002802","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":"CLaI: Collaborative Learning and Inference for low-resolution physiological signals - Validation in clinical event detection and prediction.","authors":"Hollan Haule, Ian Piper, Patricia Jones, Tsz-Yan Milly Lo, Javier Escudero","doi":"10.1109/TBME.2025.3563732","DOIUrl":"https://doi.org/10.1109/TBME.2025.3563732","url":null,"abstract":"<p><p>While machine learning (ML) techniques have been applied to detection and prediction tasks in clinical data, most methods rely on high-resolution data, which is not routinely available in most Intensive Care Units (ICUs), and perform poorly when faced with class imbalance. Here, we introduce and validate Collaborative Learning and Inference (CLaI) for detection and prediction of events from learned latent representations of multivariate physiological time series, leveraging similarities across patients. Our method offers a new way to detect and predict events using low-resolution physiological time series. We evaluate its performance on predicting intracranial hypertension and sepsis using the KidsBrainIT (minute-by-minute resolution) and MIMIC-IV (hourly resolution) datasets, respectively, comparing our approach with classification-based and sequence-to-sequence benchmarks from existing studies. Additional experiments on sepsis detection, robustness to class imbalance, and generalizability-demonstrated via seizure detection using the CHB-MIT scalp electroencephalogram dataset-confirm that CLaI effectively handles class imbalance, consistently achieving competitive performance and the highest F1 score. Overall, our approach introduces a novel method for analyzing routinely collected ICU physiological time series by leveraging patient similarity thus enabling ML interpretability through case-based reasoning.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984586","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":"IEEE Transactions on Biomedical Engineering Handling Editors Information","authors":"","doi":"10.1109/TBME.2025.3556624","DOIUrl":"https://doi.org/10.1109/TBME.2025.3556624","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 5","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}