Medical & Biological Engineering & Computing最新文献_第10页

Quantifying the development of visual expertise in medical image interpretation using fractal eye-gaze metrics. 使用分形人眼注视度量来量化医学图像解释中视觉专业知识的发展。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-09 DOI: 10.1007/s11517-026-03613-0

Ghasem Azemi, Poonam Kumari, Carlo Russo, Antonio Di Ieva

{"title":"Quantifying the development of visual expertise in medical image interpretation using fractal eye-gaze metrics.","authors":"Ghasem Azemi, Poonam Kumari, Carlo Russo, Antonio Di Ieva","doi":"10.1007/s11517-026-03613-0","DOIUrl":"10.1007/s11517-026-03613-0","url":null,"abstract":"This study presents a fractal-based framework for quantifying visual expertise using eye-gaze data. A longitudinal experiment was conducted across three consecutive semesters of the medical program at Macquarie University, where the eye movements of 13 medical students were recorded with a high-resolution eye tracker while they viewed pathological images from multiple imaging modalities. In addition, eye-tracking data from 13 consultant neurosurgeons were included as expert reference data to assess whether longitudinal changes in students' approach mirror expert-like visual behavior. Three metrics were computed: the two- and three-dimensional fractal dimensions of eye-gaze to capture spatiotemporal complexity of the eye-gaze data, and a fractal-dimension-based correlation to quantify the relationship between gaze patterns and stimulus structure. These metrics were combined into a composite Fractal Eye-Gaze Expertise Index (FEI). The results show systematic reductions in fractal complexity across sessions, indicating increasingly structured and efficient gaze strategies, with trends approaching those of experts. Crucially, conventional fixation-duration-based measures failed to consistently capture this progression, unlike the proposed fractal metrics. Statistical analyses confirm significant session-related effects for all metrics, and receiver operating characteristic curve (ROC) analysis shows that the FEI effectively discriminates longitudinal differences in visual expertise. This work demonstrates that fractal-based eye-gaze modelling provides an objective and scalable approach to characterizing visual expertise.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3075-3088"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images. 基于深度学习的三维超声图像中腹主动脉瘤和腔内血栓的分割。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2024-10-25 DOI: 10.1007/s11517-024-03216-7

Arjet Nievergeld, Bünyamin Çetinkaya, Esther Maas, Marc van Sambeek, Richard Lopata, Navchetan Awasthi

{"title":"Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images.","authors":"Arjet Nievergeld, Bünyamin Çetinkaya, Esther Maas, Marc van Sambeek, Richard Lopata, Navchetan Awasthi","doi":"10.1007/s11517-024-03216-7","DOIUrl":"10.1007/s11517-024-03216-7","url":null,"abstract":"Ultrasound (US)-based patient-specific rupture risk analysis of abdominal aortic aneurysms (AAAs) has shown promising results. Input for these models is the patient-specific geometry of the AAA. However, segmentation of the intraluminal thrombus (ILT) remains challenging in US images due to the low ILT-blood contrast. This study aims to improve AAA and ILT segmentation in time-resolved three-dimensional (3D + t) US images using a deep learning approach. In this study a \"no new net\" (nnU-Net) model was trained on 3D + t US data using either US-based or (co-registered) computed tomography (CT)-based annotations. The optimal training strategy for this low-contrast data was determined for a limited dataset. The merit of augmentation was investigated, as well as the inclusion of low-contrast areas. Segmentation results were validated with CT-based geometries as the ground truth. The model trained on CT-based masks showed the best performance in terms of DICE index, Hausdorff distance, and diameter differences, covering a larger part of the AAA. With a higher accuracy and less manual input the model outperforms conventional methods, with a mean Hausdorff distance of 4.4 mm for the vessel and 7.8 mm for the lumen. However, visibility of the lumen-ILT interface remains the limiting factor, necessitating improvements in image acquisition to ensure broader patient inclusion and enable rupture risk assessment of AAAs in the future.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2903-2916"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI4US: Revolutionizing ultrasound image processing and analysis with artificial intelligence. AI4US：用人工智能革新超声图像处理和分析。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 DOI: 10.1007/s11517-026-03667-0

Sara Moccia, Maria Chiara Fiorentino, Selene Tomassini

引用次数: 0

Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images? 将图像分辨率纳入神经网络能否提高超声图像中肾肿瘤的分类性能？

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2024-08-31 DOI: 10.1007/s11517-024-03188-8

Haihao He, Yuhan Liu, Xin Zhou, Jia Zhan, Changyan Wang, Yiwen Shen, Haobo Chen, Lin Chen, Qi Zhang

{"title":"Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?","authors":"Haihao He, Yuhan Liu, Xin Zhou, Jia Zhan, Changyan Wang, Yiwen Shen, Haobo Chen, Lin Chen, Qi Zhang","doi":"10.1007/s11517-024-03188-8","DOIUrl":"10.1007/s11517-024-03188-8","url":null,"abstract":"Deep learning has been widely used in ultrasound image analysis, and it also benefits kidney ultrasound interpretation and diagnosis. However, the importance of ultrasound image resolution often goes overlooked within deep learning methodologies. In this study, we integrate the ultrasound image resolution into a convolutional neural network and explore the effect of the resolution on diagnosis of kidney tumors. In the process of integrating the image resolution information, we propose two different approaches to narrow the semantic gap between the features extracted by the neural network and the resolution features. In the first approach, the resolution is directly concatenated with the features extracted by the neural network. In the second approach, the features extracted by the neural network are first dimensionally reduced and then combined with the resolution features to form new composite features. We compare these two approaches incorporating the resolution with the method without incorporating the resolution on a kidney tumor dataset of 926 images consisting of 211 images of benign kidney tumors and 715 images of malignant kidney tumors. The area under the receiver operating characteristic curve (AUC) of the method without incorporating the resolution is 0.8665, and the AUCs of the two approaches incorporating the resolution are 0.8926 (P < 0.0001) and 0.9135 (P < 0.0001) respectively. This study has established end-to-end kidney tumor classification systems and has demonstrated the benefits of integrating image resolution, showing that incorporating image resolution into neural networks can more accurately distinguish between malignant and benign kidney tumors in ultrasound images.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2889-2901"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

3D ultrasound shape completion and anatomical feature detection for minimally invasive spine surgery. 微创脊柱手术的三维超声形状完成及解剖特征检测。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-04-22 DOI: 10.1007/s11517-025-03359-1

Ruixuan Li, Yuyu Cai, Ayoob Davoodi, Gianni Borghesan, Emmanuel Vander Poorten

{"title":"3D ultrasound shape completion and anatomical feature detection for minimally invasive spine surgery.","authors":"Ruixuan Li, Yuyu Cai, Ayoob Davoodi, Gianni Borghesan, Emmanuel Vander Poorten","doi":"10.1007/s11517-025-03359-1","DOIUrl":"10.1007/s11517-025-03359-1","url":null,"abstract":"Ultrasound (US) with 3D reconstruction is being explored to offer a radiation-free approach to visualizing anatomical structures. Such a method could be useful for navigating and assisting minimally invasive spine surgery where direct sight on the surgical site is absent. During surgery, the pre-operative CT model and surgical plans are registered to the patient's anatomy by using intra-operative US reconstruction. However, accurate and automatic registration remains challenging. This difficulty arises from an incomplete detection of the bone geometry in US images and the challenges in identifying anatomical landmarks. To address the problem, this work presents a pipeline to automate the workflow by offering an initial CT-to-US registration. This work utilizes PointAttN for 3D shape completion that completes occluded bone structures from partial US reconstruction. This enriched point cloud is then segmented using PointNet++ to identify specific anatomical features. To train the network, synthetic 3D representations of partial views are generated from fifty CT models of the lumbar spine by simulating US physics, effectively mimicking the intraoperative scenario. The proposed work yields a mean registration error of 1.34 mm and 1.63 mm on real US reconstructions of agar phantoms and an ex vivo human spine, respectively. This comprehensive 3D representation enhances anatomical feature interpretation, enabling robust automatic registration. The clinical potential of this framework merits further investigation in pre-clinical trials.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3005-3018"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Multimodal graph learning for chagas disease classification. 恰加斯病分类的多模态图学习。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-17 DOI: 10.1007/s11517-026-03631-y

Gabriel Carcedo-Rodríguez, Erik Molino-Minero-Re, Jorge Perez-Gonzalez, Nidiyare Hevia-Montiel

{"title":"Multimodal graph learning for chagas disease classification.","authors":"Gabriel Carcedo-Rodríguez, Erik Molino-Minero-Re, Jorge Perez-Gonzalez, Nidiyare Hevia-Montiel","doi":"10.1007/s11517-026-03631-y","DOIUrl":"10.1007/s11517-026-03631-y","url":null,"abstract":"The accurate classification of Chagas disease stages based on Trypanosoma cruzi infection in experimental models faces challenges due to the scarcity of data and the high dimensionality of diagnostic sources. This study proposes an architecture based on Graph Attention Networks on fully connected graphs to combine multimodal features (Electrocardiogram, Echocardiogram, Doppler, and ELISA). A Variational Graph Autoencoder was used to implement a data augmentation strategy that reduces overfitting and optimizes generalization, producing synthetic subjects with a biological covariance structure. The results demonstrate that the proposed methodology is effective compared to state-of-the-art classifiers, achieving an AUROC of 100% in the identification of infection stage through multimodal fusion and significantly optimizing performance in complex modalities such as Doppler. The pathophysiological consistency of the model's decisions was also validated using an interpretability scheme (GNNExplainer), which allowed the detection of important clinical features and established GATs as a robust method in the classification of Trypanosoma cruzi infection stages.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3181-3198"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACE-Net: A-line coordinates encoding network for vascular structure segmentation in ultrasound images. ACE-Net：用于超声图像血管结构分割的a线坐标编码网络。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-04-02 DOI: 10.1007/s11517-025-03323-z

Beatriz Farola Barata, Guiqiu Liao, Gianni Borghesan, Keir McCutcheon, Johan Bennett, Benoit Rosa, Michel de Mathelin, Florent Nageotte, Michalina J Gora, Jos Vander Sloten, Emmanuel Vander Poorten, Diego Dall'Alba

{"title":"ACE-Net: A-line coordinates encoding network for vascular structure segmentation in ultrasound images.","authors":"Beatriz Farola Barata, Guiqiu Liao, Gianni Borghesan, Keir McCutcheon, Johan Bennett, Benoit Rosa, Michel de Mathelin, Florent Nageotte, Michalina J Gora, Jos Vander Sloten, Emmanuel Vander Poorten, Diego Dall'Alba","doi":"10.1007/s11517-025-03323-z","DOIUrl":"10.1007/s11517-025-03323-z","url":null,"abstract":"Ultrasound (US) imaging enables the evaluation of vascular structures in real time, and it can provide morphological and pathological information during US-guided procedures. Automatic prediction of vascular structure boundaries can help clinicians in locating and measuring target structures more accurately and efficiently. Most existing US segmentation methods use per-pixel classification or regression, which require post-processing to obtain contour coordinates. In this work, we present ACE-Net, a novel approach that directly predicts the contour coordinates for every scanning line (A-line) in US images. ACE-Net combines two main modules: a boundary regression module that predicts the upper and lower coordinates of the target area for each A-line, and an A-line classification module that determines whether an A-line belongs to the target area or not. We evaluated our method on three clinical US datasets using, among others, dice similarity coefficient (DSC) and inference time as performance metrics. Our method outperformed state-of-the-art segmentation methods in inference time while achieving superior or comparable performance in DSC. ACE-Net is publicly available at https://github.com/bfarolabarata/ace-net .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2967-2988"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and clinical evaluation of a virtual reality-based system for automated ocular deviation measurement. 基于虚拟现实的自动眼偏测量系统的设计与临床评价。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-11 DOI: 10.1007/s11517-026-03623-y

Lijing Chen, Seo Hyeon Jeong, Hwan Heo, Sang Woo Park, Penghua Zhang, Gyuhae Park

{"title":"Design and clinical evaluation of a virtual reality-based system for automated ocular deviation measurement.","authors":"Lijing Chen, Seo Hyeon Jeong, Hwan Heo, Sang Woo Park, Penghua Zhang, Gyuhae Park","doi":"10.1007/s11517-026-03623-y","DOIUrl":"10.1007/s11517-026-03623-y","url":null,"abstract":"Accurate measurement of ocular deviation is essential for strabismus assessment and treatment monitoring. Conventional methods such as the prism alternating cover test require trained examiners and substantial patient cooperation, which may introduce inter-examiner variability and limit measurement standardization. We developed a virtual reality (VR)-based system for automated horizontal ocular deviation measurement. The system simulates a prism alternating cover test in a controlled VR environment to guide fixation and record eye movement videos under standardized visual conditions. Recorded videos are processed using a multi-stage pupil localization and tracking pipeline to maintain robust performance under partial occlusion and blinking, and extracted pupil trajectories are converted into quantitative estimates of horizontal ocular deviation. In a pilot clinical evaluation, the system achieved over 93% agreement with clinician diagnoses in identifying horizontal ocular deviation categories, with a mean angular error of 0.35 ± 1.09° relative to reference clinical assessments. These findings demonstrate the feasibility of a VR-based automated system for standardized horizontal ocular deviation measurement and support its potential value for objective strabismus-related assessment.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3101-3114"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

HDDI-Net: Hierarchical dual-domain interaction network for robust and efficient ultrasound lesion segmentation. hdi - net：用于稳健高效超声病灶分割的分层双域交互网络。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-15 DOI: 10.1007/s11517-026-03632-x

Hao Li, YuXin Yang, Chenxu Wang

{"title":"HDDI-Net: Hierarchical dual-domain interaction network for robust and efficient ultrasound lesion segmentation.","authors":"Hao Li, YuXin Yang, Chenxu Wang","doi":"10.1007/s11517-026-03632-x","DOIUrl":"10.1007/s11517-026-03632-x","url":null,"abstract":"Lesion segmentation in B-mode ultrasound remains a critical challenge due to speckle interference, low tissue contrast, and constrained computational budgets in portable devices. Accurate delineation of lesions is essential for diagnosis, treatment planning, and longitudinal monitoring. Existing lightweight spatial models often fail to suppress speckle-dominated noise, whereas transformer-based methods exceed point-of-care computational limits. To address these challenges, we propose HDDI-Net, a Hierarchical Dual-Domain Interaction Network integrated into a macro-micro region-of-interest pipeline. HDDI-Net combines multi-kernel spatial feature extraction with discrete cosine transform (DCT)-guided frequency gating to effectively suppress speckle while preserving morphological integrity. At the network bottleneck, a Prototype-Guided Semantic Consistency (PGSC) module approximates global context without high computational cost. Extensive experiments on the BUSI and TN3K datasets, along with zero-shot cross-domain evaluation on UDIAT, demonstrate that HDDI-Net achieves improved accuracy-efficiency trade-offs compared with evaluated lightweight CNNs and transformer-based segmentation models, operating with only 1.7M parameters and a moderate computational footprint of 3.3 GFLOPs, achieving up to 5.0 percentage-point IoU improvement in zero-shot evaluation and up to 3.9 percentage-point Dice improvement over the strongest competing baseline. The proposed approach shows promising potential for resource-constrained ultrasound lesion segmentation and may support future integration into computer-aided diagnosis pipelines after further deployment-oriented optimization and clinical validation. To facilitate reproducibility, the source code and pre-trained models are publicly available at https://github.com/DPDP-root/HDDI-Net.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3161-3180"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148450208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

NewRep: a classification model for online pattern recognition based on HD-EMG signals. NewRep：基于HD-EMG信号的在线模式识别分类模型。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-21 DOI: 10.1007/s11517-026-03630-z

Lizhi Pan, Shiwei Chen, Jianmin Li, Jinhua Li

{"title":"NewRep: a classification model for online pattern recognition based on HD-EMG signals.","authors":"Lizhi Pan, Shiwei Chen, Jianmin Li, Jinhua Li","doi":"10.1007/s11517-026-03630-z","DOIUrl":"10.1007/s11517-026-03630-z","url":null,"abstract":"High density (HD) electromyography (EMG) signals have been widely used for pattern recognition (PR) in myoelectric control, particularly for gesture recognition. Although neural networks generally outperform traditional machine learning models in EMG PR, the limited accuracy in recognition remains a critical challenge. To further improve the online classification of neural networks in EMG PR, this study establishes a new neural network named NewRep by introducing the self-attention mechanism module into the existing RepViT, a lightweight convolutional neural network inspired by vision transformer designs. Online gesture classification was performed with NewRep, RepViT, and linear discriminant analysis. Eighteen non-disabled subjects were recruited and tested in this study. For each subject, 256 channels of EMG signals were acquired from four forearm muscles: flexor digitorum superfcialis, flexor carpi radialis, extensor digitorum, and extensor carpi radialis brevis. Twenty hand and wrist motions were performed during offline and online experiments. Five metrics were employed to evaluate performances of the three models, including offline classification accuracy, motion selection time, motion completion time, motion completion rate, and online classification accuracy. Compared with RepViT and linear discriminant analysis, the proposed NewRep showed a significant improvement in motion completion time (decreased by 0.053 s and 0.117 s, respectively) and online classification accuracy (increased by 2.91% and 6.24%, respectively). The results demonstrated that NewRep effectively enhanced the real-time decoding capability of myoelectric control, offering a promising approach for practical applications.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3199-3213"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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