IEEE Transactions on Biomedical Engineering最新文献

{"title":"ReCIL: Rehearsal-Based Class Incremental Learning for Cross-Subject Motor Imagery Classification.","authors":"Xinyao Yang, Jiayu An, Dingkun Liu, Xinping Xu, Dongrui Wu","doi":"10.1109/TBME.2026.3721154","DOIUrl":"10.1109/TBME.2026.3721154","url":null,"abstract":"<p><strong>Objective: </strong>Electroencephalography (EEG) based motor imagery (MI) is a classic brain-computer interface (BCI) paradigm, which can be used in neuro-rehabilitation and device control. Existing MI classification approaches only consider the scenario that the number of MI classes is pre-determined and fixed; when new MI classes arrive, data for all classes must be recollected and the model retrained, which is inefficient, and sometimes impossible, as the training data for previous classes may be lost or inaccessible due to privacy concern.</p><p><strong>Methods: </strong>This paper proposes rehearsal-based class incremental learning (ReCIL) for cross-subject MI classification, where the model learns new MI classes sequentially, and the test subjects are different from the training subjects. ReCIL uses Euclidean Alignment to reduce the inter-subject EEG data distribution shift, global-local replay to preserve the knowledge of old tasks, and dimensionality reduction to facilitate reliable similarity computation among EEG samples.</p><p><strong>Results: </strong>Experiments on three public MI datasets showed that ReCIL achieved a good balance between plasticity and stability.</p><p><strong>Significance: </strong>To our knowledge, this is the first study on cross-subject class incremental learning for MI classification, offering a practical solution for incremental class expansion without retraining from scratch.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684358","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":"Adaptive Hiking-Time Prediction Through State-Dependent Energy Regulation.","authors":"Kristian Dalland, Souma Chowdhury, Karthik Dantu, Ehsan T Esfahani","doi":"10.1109/TBME.2026.3720250","DOIUrl":"10.1109/TBME.2026.3720250","url":null,"abstract":"<p><p>Accurate prediction of hiking time is important for assessing human performance, physical activity planning, and time-sensitive field operations. Most existing approaches are environment-driven and do not account for how human capacity evolves during prolonged walking. We present a human-aware hiking-time prediction model that integrates energy expenditure with a state dependent control-theoretic energy-reserve abstraction to regulate pacing throughout a hike. The reserve mechanism serves as a computational control construct that governs cumulative workload and recovery during prolonged activity, rather than direct measurement or inference of physio logical state. The model operates without subject-specific calibration and captures cumulative workload effects over extended durations. We evaluated this approach on 2228 publicly available GPS-tracked hikes and benchmarked it against widely used environment-driven hiking-time formulations and a non-adaptive human-aware baseline. In corporating state-dependent pacing significantly improved prediction accuracy and agreement with observed hiking times. The adaptive model achieved the lowest mean absolute percent error and bias, maintained near-proportional scaling with observed hiking time, and attained the highest concordance correlations. Performance gains were greatest for longer, higher-demand hikes, while static pacing remained sufficient under short, low-demand conditions.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678310","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":"Prediction of Tissue Ablation Zones in Asymmetric High-Frequency Irreversible Electroporation Incorporating Dynamic Conductivity and Dielectric Dispersion.","authors":"Bokai Chen, Yueyue Xiao, Liang Wang, Chunxiao Chen, Ming Lu","doi":"10.1109/TBME.2026.3720201","DOIUrl":"10.1109/TBME.2026.3720201","url":null,"abstract":"<p><strong>Objective: </strong>Asymmetric pulse waveforms represent an optimized treatment protocol for high-frequency irreversible electroporation (H-FIRE), offering a lower ablation threshold compared to symmetric waveforms. However, the impact of waveform asymmetry has not been considered in current numerical models aimed at predicting tissue ablation zones. This study aims to develop an asymmetric waveform ablation area prediction model incorporating dynamic conductivity and dielectric dispersion effects (AW-DCDE) to predict ablation zones under varying asymmetric conditions.</p><p><strong>Methods: </strong>A linear scaling parameter relating pulse asymmetry to the ablation threshold was integrated into a modified Heaviside conductivity function. Dielectric dispersion was captured via a fourth-order Debye model. Variations in electric field intensity and dielectric dispersion were analyzed at pulse widths of 5 μs and 10 μs across different asymmetry levels. Finally, potato tissue experiments were conducted to validate the AW-DCDE model.</p><p><strong>Results: </strong>Increasing waveform asymmetry leads to a reduction in electric field intensity and polarization current, but a significant expansion of the ablation area. Statistical analysis confirmed no significant difference (p > 0.05) between AW-DCDE predictions and experimental results. Dynamic conductivity was found to have a greater influence on the predicted ablation area than dielectric dispersion.</p><p><strong>Conclusion: </strong>Waveform asymmetry significantly affects ablation area and electrical parameters.</p><p><strong>Significance: </strong>The AW DCDE model provides a theoretical framework for optimizing clinical H-FIRE treatment protocols.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148701204","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":"SAM-HovNet: SAM-Enhanced Multi-Class Nucleus Segmentation and Classification in Hepatocellular Carcinoma Slides.","authors":"Hongbo Liu, Hongming Xu, Ilkka Polonen, Chao Tang, Mingliang Zhang, Su Yao, Cheng Lu, Fengyu Cong","doi":"10.1109/TBME.2026.3720392","DOIUrl":"10.1109/TBME.2026.3720392","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a leading cause of cancer-related mortality. Manual nuclear analysis in HCC histopathology is labor-intensive and prone to interobserver variability, underscoring the need for robust automated solutions. Although deep learning methods for nuclei segmentation and classification have progressed substantially, their performance on liver tissue remains suboptimal. To address this limitation, we propose SAM-HovNet, a unified framework that enhances HoverNet by integrating the SAM foundation model, Squeeze-and-Excitation modules, and multi-scale feature representations for multi-class nucleus segmentation and classification in HCC slides. Experiments on the HCCNuc dataset demonstrate that SAMHovNet achieves an image-level mean F1-score of 0.577 and an image-level mean panoptic quality (PQ) of 0.521. Furthermore, SAM-HovNet demonstrates strong generalizability, significantly outperforming competing approaches on the public PanNuke dataset. These results highlight the robustness of SAM-HovNet and its potential to support downstream nuclear characterization in HCC histopathology.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148701019","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":"Hybrid Modeling of Capacitive Human Body Communication Systems to Capture Channel Variability.","authors":"Cheng Tang, Dang Le, Yasuo Morimoto, Djordje Tujkovic, Benjamin Cook, Mingjie Ma, Jiang Zhu, Patrick P Mercier","doi":"10.1109/TBME.2026.3719639","DOIUrl":"10.1109/TBME.2026.3719639","url":null,"abstract":"<p><strong>Objective: </strong>The purpose of this contribution is to estimate the path-loss of capacitive human body communication (cHBC) systems under varying channel conditions.</p><p><strong>Methods: </strong>Accurate modeling of cHBC channel characteristics is critical for system design, and yet, channel characteristics are a complex function of environment, electrode geometries, and posture that are difficult to model. Although full-field electromagnetic simulations can accurately capture such complexity, they are slow to run, making it challenging to assess channel statistics quickly. This paper presents a hybrid model that combines the strengths of full-field electromagnetic simulations, measurements, and circuit model analysis to enable rapid, yet accurate, assessments of cHBC channel characteristics across all the aforementioned variations, with a focus on posture variations as the most common source of channel variation.</p><p><strong>Results: </strong>The proposed model is investigated using a combination of analytical and simulation-based approaches. Experiments with miniaturized battery-operated devices are also conducted to validate the model's results. Simulation results show agreement with measurements across channel variations, with the worst-case mean error magnitude $< $ 2.40 dB.</p><p><strong>Conclusion: </strong>Channel variations in cHBC can be significant and pose a major challenge in predicting channel behavior. Therefore, a model that can accurately estimate the channel loss both across frequencies and channel variations is imperative.</p><p><strong>Significance: </strong>cHBC is an attractive candidate for building a more secure and energy-efficient body area network. This modeling approach supports a more reliable link budget calculation under varying cHBC channel characteristics, which helps inform efficient designs of cHBC circuits under more realistic use cases.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669386","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":"High-Sensitivity Ferrite Magnetometer for Low-Dose Magnetic Sentinel Lymph Node Detection.","authors":"Qibin Wang, Xinyue Chen, Lei Li, Kaixiong Yang, Lingwen Hou, Wang Shu, Zhongyi Wu, Xin Feng, Shouping Zhu","doi":"10.1109/TBME.2026.3719508","DOIUrl":"https://doi.org/10.1109/TBME.2026.3719508","url":null,"abstract":"<p><strong>Objective: </strong>Magnetic tracer-guided sentinel lymph node (SLN) biopsy offers a radiation-free alternative for nodal staging in breast cancer and other solid tumors. However, current handheld magnetometers often require high superparamagnetic iron oxide nanoparticle (SPION) doses due to limited sensitivity and detection depth, leading to skin staining and MRI artifacts.</p><p><strong>Methods: </strong>We developed FerriMag, a handheld ferrite magnetometer integrating a central high-permeability Mn-Zn ferrite core to enhance magnetic excitation and signal reception. Lock-in amplification and bandpass filtering were employed to suppress interference.</p><p><strong>Results: </strong>The ferrite core increased the excitation field strength by 11-fold and receiving-coil sensitivity by 1.7-fold. FerriMag achieved a detection limit of 21.6 ng Fe and reliably detected 0.5 μg Fe at a depth of 10 mm. In a rabbit model, accurate SLN localization was obtained with only 0.05 mL (0.5 mg Fe) of SPIONs, without visible skin staining. Metastatic nodes exhibited significantly reduced SPION uptake (p = 0.0005) and slower signal rise(p = 0.038) compared to non-metastatic nodes.</p><p><strong>Conclusion: </strong>FerriMag improves the sensitivity and detection depth of handheld magnetic SLN detection, enabling reliable low dose SPION-based SLN localization in vivo.</p><p><strong>Significance: </strong>This system provides a low-cost, radiation-free, and clinically translatable platform for low-dose magnetic SLN detection, with potential applicability beyond breast cancer and possible utility for intraoperative nodal assessment.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669414","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":"FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-Low Power Microcontroller","authors":"Anna Tegon;Nicolas Lehmann;Yawei Li;Andrea Cossettini;Luca Benini;Thorir Mar Ingolfsson","doi":"10.1109/TBME.2026.3683482","DOIUrl":"10.1109/TBME.2026.3683482","url":null,"abstract":"<italic>Objective:</i> To enable continuous, long-term neuro-monitoring on wearable devices by overcoming the computational bottlenecks of Transformer-based Electroencephalography (EEG) foundation models and the quantization challenges inherent to State-Space Models (SSMs). <italic>Methods:</i> We present FEMBA, a bidirectional Mamba architecture pre-trained on over 21,000 hours of EEG. We introduce a novel Physiologically-Aware pre-training objective, consisting of a reconstruction with low-pass filtering, to prioritize neural oscillations over high-frequency artifacts. To address the activation outliers common in SSMs, we employ Quantization-Aware Training (QAT) to compress the model to 2-bit weights. The framework is deployed on a parallel ultra-low-power RISC-V microcontroller (GAP9) using a custom double-buffered memory streaming scheme. <italic>Results:</i> The proposed low-pass pre-training improves downstream AUROC on TUAB from 0.863 to 0.893 and AUPR from 0.862 to 0.898 compared to the best contrastive baseline. QAT successfully compresses weights with negligible performance loss, whereas standard post-training quantization degrades accuracy by approximately <bold>30%</b>. The embedded implementation achieves deterministic real-time inference (<bold>1.70 s</b> per 5 s window) and reduces the memory footprint by <bold>74%</b> (to <inline-formula><tex-math>$approx$</tex-math></inline-formula>2 MB), achieving competitive accuracy with up to <bold>27×</b> fewer FLOPs than Transformer benchmarks. <italic>Conclusion:</i> FEMBA demonstrates that Mamba-based foundation models can be effectively quantized and deployed on extreme-edge hardware without sacrificing the representation quality required for robust clinical analysis. <italic>Significance:</i> This work establishes the first full-stack framework for deploying large-scale EEG foundation models on ultra-low-power wearables, facilitating continuous, SSM based monitoring for epilepsy and sleep disorders.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2746-2757"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147672791","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":"CogniFuse and Multimodal Deformers: An Extended Study on Benchmarking and Modeling Biosignal Fusion","authors":"Anthony Richardson;Michael Beetz;Tanja Schultz;Felix Putze","doi":"10.1109/TBME.2026.3691190","DOIUrl":"10.1109/TBME.2026.3691190","url":null,"abstract":"Human physiological signals reflect complex biological processes and provide important insights into physical and mental states. Extracting such information from biosignal data collected during everyday activities holds great potential for real-time monitoring of physical and mental states, but is challenging due to noise and artifacts. To address this, we introduce CogniFuse, the first publicly available multi-task benchmark for multimodal biosignal fusion in such unconstrained environments. In addition, we develop a comprehensive benchmarking pipeline that emphasizes comparability, reproducibility, accessibility, and usability, while demonstrating robustness across architectures, tasks, and model sizes. For many biosignals, particularly electrophysiological signals, information in different frequency bands is critical for assessing physiological states. Motivated by this, we propose a family of Multimodal Deformer models that capture multi-level power features along with both long- and short-term temporal dependencies across multiple biosignal modalities. In particular, our Multi-Channel Deformer achieves the highest average benchmark score, outperforming all models of comparison. By advancing multimodal biosignal fusion in everyday settings, this work supports real-time monitoring of physical and mental states outside highly controlled clinical conditions. To ensure full transparency and reproducibility, and to facilitate future research, all code and data are made publicly available.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2723-2735"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11511773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837278","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}

{"title":"EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture","authors":"Seth Donahue;Irina Djuraskovic;Kunal Shah;Fabian Sinz;Ross Chafetz;R. James Cotton","doi":"10.1109/TBME.2026.3691128","DOIUrl":"10.1109/TBME.2026.3691128","url":null,"abstract":"<italic>Objective:</i> This study evaluates the calibration and accuracy of a probabilistic MMMC method. Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerless motion capture (MMMC) require that, in addition to being accurate, these systems produce reliable confidence intervals to indicate how accurate they are for any individual. Building on our prior work utilizing variational inference to estimate estimate biomechanical variables against clinical gold-standards. <italic>Methods:</i> We analyzed data from 68 participants across two institutions, validating the model against an instrumented walkway and standard marker-based motion capture. We measured the calibration of the confidence intervals using the Expected Calibration Error (ECE). <italic>Results</i>: the model demonstrated reliable calibration, yielding ECE values generally <inline-formula><tex-math>$&lt;!0.1$</tex-math></inline-formula> for both step and stride length and bias-corrected gait kinematics. We observed a median step and stride length error of <inline-formula><tex-math>$sim!16$</tex-math></inline-formula> mm and <inline-formula><tex-math>$sim!12$</tex-math></inline-formula> mm respectively, with median bias-corrected kinematic errors ranging from <inline-formula><tex-math>$1.5^{circ }$</tex-math></inline-formula> to <inline-formula><tex-math>$3.8^{circ }$</tex-math></inline-formula> across lower extremity joints. Consistent with the calibrated ECE, the magnitude of the model’s predicted uncertainty correlated strongly with observed error measures. <italic>Conclusion:</i> These findings indicate that, as designed, the probabilistic model reconstruction quantifies epistemic uncertainty, shown by low absolute error and calibrated uncertainty. <italic>Significance:</i> These findings highlight the potential to identify unreliable outputs without the need for concurrent ground-truth instrumentation through the use of this probabilistic model.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2889-2898"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837299","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":"Modeling Early Oxygenation Trajectory in PARDS From High-Frequency Mechanical Ventilation Signals Using Deep Sequence Architectures","authors":"Wenting Liu;Joseph G. Kohne;Emily Wittrup;Kayvan Najarian","doi":"10.1109/TBME.2026.3700483","DOIUrl":"10.1109/TBME.2026.3700483","url":null,"abstract":"<italic>Objective:</i> PARDS causes substantial morbidity in PICU. We investigated whether high-frequency ventilator waveforms from first hour of invasive ventilation contain predictive information for oxygenation trajectory. <italic>Methods:</i> We analyzed mechanically ventilated pediatric patients using engineered statistical features and learned representations from high-frequency ventilator signals, including breath-by-breath flow waveforms. Engineered features included early oxygenation indices and ventilator variables derived from time- and frequency-domain analyses. Learned representations were obtained from 1D-CNN embeddings with PCA-based dimensionality reduction. We evaluated two tasks: 12-hour OSI regression and classification of mild vs moderate-or-higher impairment (OSI). Five sequence models (RNN, LSTM, GRU, Transformer, Mamba) and nine feature configurations were assessed using repeated cross-validation and validation on held-out and temporally separated cohorts. <italic>Results:</i> For classification, the best cross-validated AUROC was 0.819 ± 0.030. Performance remained consistent across independent cohorts, with AUROC values of 0.787–0.823 on the test cohort and 0.808–0.832 on the temporal validation cohort, and Brier scores <inline-formula><tex-math>$sim$</tex-math></inline-formula>0.12–0.15. For regression, the best cross-validated RMSE was 2.45 ± 0.81. Test RMSE ranged from 2.99 to 3.48 OSI units and validation RMSE from 2.60 to 2.85. <italic>Conclusion:</i> High-frequency ventilator waveforms from the first hour of mechanical ventilation contain measurable information about short-horizon oxygenation trajectory in PARDS. <italic>Significance:</i> These findings support feasibility of modeling early oxygenation trajectory using routinely available ventilator waveform data and motivate prospective multi-institutional validation.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2709-2722"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148163047","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}