IEEE Transactions on Biomedical Engineering最新文献

Objective: Upper limb loss due to traumatic injury or disease poses significant challenges to autonomy, daily function, and workforce reintegration, profoundly impacting overall quality of life. While myoelectric prosthetic hands have the potential to restore dexterity, many users discontinue use due to limited functionality and durability.This manuscript describes the design and rationale of an ongoing clinical trial aimed at addressing these gaps in real-world settings.

Methods: We searched for completed and ongoing clinical trials on ClinicalTrials.gov to study their structure and their gaps, and then we presented the protocol of our ongoing clinical trial. This protocol outlines a randomized crossover clinical trial enrolling 36 adults with upper limb loss to evaluate two multi-articulated myoelectric prosthetic hands.

Results: Our review of clinical trials revealed that the unique strength of our design is the integration of standardized laboratory tests, extended daily use, onboard usage data, and validated satisfaction surveys. We provided a detailed description of all design choices and rationale of the ongoing clinical study.

Conclusion: The comparison between our design and the design of other studies indicates that our design is unique in the integration of biomechanical assessments, real-world usage monitoring, and user-reported outcomes. This clinical trial should be capable of assessing if one specific device design can offer clinically meaningful advantages over another.

Significance: The design of our clinical trial could inform the design of clinical trials targeting the optimization of prostheses and their acceptance by prosthetic users.

Objective: transkeyhole microsurgery for spinal cord tumors requires intraoperative imaging guidance to ensure safe and effective tumor resection. Although optical endoscopy has been widely adopted in clinical settings, it's limited to visualizing superficial structures. Endoscopic ultrasound (EUS) offers a promising alternative. However, EUS transducers are typically fabricated from high-frequency arrays, which provide limited imaging depth and field of view. In addition, the high cost of the transducers and complicated sterilization further restrict their use in surgery.

Methods: this paper introduces an economical single-element US transducer that utilizes electromagnetic actuation operating in a resonant scanning mode. An image-based method is proposed to correct the resulting nonlinear scanning. Two prototypes were developed, having outer diameters of 14 (T14) and 9 (T9) mm. The imaging performance of the transducers was evaluated by wire phantoms, tissue-mimicking phantom, ex-vivo sheep spine, and in-vivo rabbits.

Results: The T14 and T9 achieved scanning angles over 70$^circ$ and approximately 60$^circ$, respectively, with the former maintaining a lateral resolution of 248 $mu$m and the latter yielding an optimal contrast-to-noise ratio of 2.43. The captured US imaging clearly visualized dural sac and unilateral nerve roots in sheep spine and enabled the accurate identification of subdural hemorrhage and key anatomy in rabbits.

Conclusion: the electromagnetically actuated transducer achieves a wide scanning range despite its compact size, showing great promise for surgery by facilitating the identification of subdural anatomy and enabling customized dural opening strategies.

Significance: cost reduction enables the feasible use of the transducer as a single sterile device in surgical settings.

Objective: We present a novel wearable T-shaped ultrasound (WTSUS) patch for simultaneous short-axis and long-axis imaging monitoring of carotid artery in situ within the same cardiac cycle to measure the carotid blood flow volume.

Methods: WTSUS patch consists of two same ultrathin ultrasound transducer arrays with a center frequency of 8.5 MHz. The B-mode imaging provides real-time measurement of the cross-section area of the carotid artery, while Doppler imaging captures velocity time integral.

Results: WTSUS patch exhibits a total thickness of 1.3 mm and a wide -6 dB bandwidth of 65%. The axial and lateral resolutions at a depth of 20 mm were 0.37 mm and 0.45 mm, respectively. In vitro flow volume experiments showed that the maximum measurement deviation using WTSUS patch was 6.3%. In vivo imaging of the human common carotid artery exhibited good agreement with a commercial ultrasound system, demonstrating the reliability of WTSUS-based wearable ultrasound system.

Conclusion: This study exhibits a wearable ultrasound imaging patch with a reliable continuous monitoring of the carotid blood flow volume that is also comfortable and easy to wear.

Significance: This work can provide a novel and reliable solution for noninvasive cardiac output estimation, with significant potential for applications in critical care and continuous monitoring of dynamic blood flow volume.

Objective: Fluorescence cell counting is vital in biomedical research, yet existing automated methods lack sufficient adaptability and accuracy, leading to persistent errors in complex microscopy images. This study aims to propose an adaptive, interactive approach to effectively overcome these limitations.

Methods: We introduce the Adaptive Interactive Cell Counting (AICC) framework, combining a coordinate-based prediction module with user-guided correction. Specifically, we develop two novel global correction algorithms, Proposal Expansion (PE) and Prediction Filtering (PF), coupled with a new RGB-Aware Structural Similarity (RGB-Aware SSIM) metric to identify visually similar regions and efficiently propagate minimal user corrections. Additionally, we release NEFCell, a new high-resolution fluorescence microscopy dataset designed explicitly for evaluating interactive cell counting methods.

Results: Extensive evaluations show that AICC significantly surpasses current state-of-the-art methods, reducing counting errors by up to 36.8% compared to non-interactive approaches and up to 65.3% compared to existing interactive methods, while improving localization accuracy by 7.3% on average and significantly minimizing interaction time.

Conclusion: The proposed AICC framework substantially enhances accuracy and reduces effort required for fluorescence cell counting, proving its effectiveness in integrating automation with user expertise.

Significance: AICC represents a valuable tool for biomedical researchers and clinicians, facilitating precise and efficient cell analyses in complex experimental and clinical contexts.

Tactile stimulation, especially electrotactile stimulation, have been a subject of interest in recent literature for machine-to-human communication (M2HC) of electronically gathered information for the purpose of augmenting and improving the human experience. Electrotactile is a direct noninvasive method for peripheral nerve stimulation that provides a pathway for communication with the brain. However, the widespread use of electrotactile as an M2HC pathway is hampered by the availability and ease of use of mainstream, visual and audio, communication methods and technological challenges with electrotactile stimulation that must be resolved, such as skin condition dependency, neural adaptation, and the lack of a framework for producing consistent electrotactile M2HC. As such, this paper (1) reviews the scientific and engineering literature associated with electrotactile stimulation and associated electronics with a goal of converging disciplinary knowledge of this topic, (2) summarizes recent advances and open challenges in electrotactile stimulation, and (3) discusses available techniques and introduces a unifying model for icon-based electrotactile communication. In contrast to prior review papers on the subject, this paper uniquely focuses on defining electrotactile stimulation as a method for robust machine-to-human communication while compiling and discussing relevant engineering, physiology, and neuroscience issues, thus providing a comprehensive understanding of electrotactile M2HC for the IEEE community.

Objective: Micrometer-scale precision is vital for patient safety in ophthalmic surgery. Recent advancements in instrument-integrated optical sensors aim to accurately measure instrument-to-tissue distances. However, the reliability of these measurements is often hindered by segmentation errors caused by artifacts in the signal.

Methods: We propose a deep learning framework to identify optical coherence tomography (OCT) M-scans that fall outside the expected distribution. Our approach incorporates adaptive remote center of motion (RCM)-informed retinal modeling along with time series analysis to effectively detect and rectify segmentation errors. This method estimates retinal distances and their associated confidence levels by leveraging retinal models, instrument positions, and validated distance data.

Results: Validation tests conducted on ex vivo human eyes reveal that our pipeline achieves an 88.8% accuracy in identifying out-of-distribution (OOD) measurements. Furthermore, distance estimation improved by 89% and 93% when compared to two existing methods, resulting in an overall mean absolute error (MAE) of less than 40 μm across diverse conditions, including scans with blood and obstructions.

Conclusion: This research enhances the accuracy of instrument-to-retina distance estimation, thereby contributing to improved patient safety in ophthalmic surgical procedures.

Significance: The proposed method has potential applications beyond ophthalmic surgery, offering benefits to a variety of surgical disciplines and sensorequipped instruments.

Objective: To address the critical challenge of providing accurate, real-time lower-limb joint torque estimation across diverse locomotion conditions for adaptive human-exoskeleton interaction.

Methods: We developed a novel dual-branch architecture that synergizes temporal convolutional networks (TCN) and transformers to process surface electromyography and kinematic data. The TCN captures local temporal dynamics, while the transformer extracts global dependencies. A joint-specific task-aware residual fusion mechanism was introduced to dynamically synthesize these features, employing residual enhancement to adapt precisely to the distinct biomechanics of individual joints.

Results: Validated across twelve diverse locomotion patterns, the framework achieved root mean square errors (Nm/kg) and Pearson correlation coefficients of 0.1655/0.9904 (ankle), 0.1405/0.9588 (knee), and 0.1975/0.9698 (hip). It maintained a 4.2912 ms latency and showed strong adaptability on public datasets.

Conclusion: The proposed method effectively balances high estimation accuracy with the strict computational efficiency needed for real-time applications, successfully addressing previous issues in adapting to dynamic environments.

Significance: This work advances biomedical engineering by providing a fast, reliable solution for adaptive exoskeleton torque control, significantly enhancing seamless and natural human-robot interaction in assistive exoskeleton technologies.

Background: Frozen section (FS) tissue assessment is essential for guiding intraoperative surgical decision-making in oncology, particularly in procedures such as pancreatic ductal adenocarcinoma (PDAC) resections, where margin status critically impacts patient survival. The current gold standard, (FS), while widely used, suffers from notable limitations, including tissue artifacts, dependence on specialized expertise, and slow turnaround times, resulting in sampling errors and false negatives.

Methods: To address these challenges, we present a novel approach for automatic cancer identification in fresh tissue biopsies using mul tispectral Mueller Matrix (MM) polarimetry. Our custom-built multispectral MM polarimeter captures polarization-resolved imaging across multiple wavelengths, enabling pixel-level analysis of tissue microstructure without staining or histology sectioning. Our approach thus allows for assessments in quasi-real time. From these, we propose a deep learning model that uses MM data collected from PDAC patients to distinguish cancerous from non-cancerous biopsies to assess samples automatically.

Results: Experimental results demonstrate classification performance comparable to RFS assessments performance found in clinical routine, with enhanced diagnostic speed. We show that our approach is consistent and coherent against pixel-wise annotations from histology slides.

Conclusion: This study highlights the potential of MM polarimetry combined with machine learning as a viable, label-free alternative for real-time intraoperative cancer detection.

Objective: Physiological time series reflect the underlying behavior of physiological systems. In this paper, we introduce a novel patching with sequential updating for Bayesian nonparametric spectral estimation (PBNSE) to enhance spectral estimation and interpretation of imperfect physiological time series with fragmented, noncontiguous segments.

Methods: PBNSE incorporates four key strategies: (1) modeling patches as patch-specific Gaussian processes (GPs); (2) patch-dependence, where each patch involves a joint GP with a shared kernel, capturing both observation and spectral dependencies across all patches; (3) sequential parameter shift that transfers knowledge between patches while maintaining computational traceability; and (4) aggregating patch-level posterior spectra into a unified power spectral density (PSD) estimate and computing the expectation of the PSD in a closed form.

Results: Extensive experiments demonstrate significant improvements in spectral accuracy and robustness compared to state-of-the-art methods such as BNSE, multitaper, periodogram, Lomb-Scargle, functional kernel learning (FKL), and variational sparse spectrum (SVSS).

Conclusion: PBNSE addresses key challenges in physiological signal analysis, including irregular sampling, incomplete signal, and varying noise.

Significance: The widespread adoption of PBNSE in physiological signal research has the potential to enhance the accuracy of spectral estimation and improve the robustness of interpreting complex, real-world physiological time series.

Objective: This paper presents a two-stage machine learning model for electrographic seizure detection using wearable single-channel scalp electroencephalogram (EEG) sensors.

Methods: The algorithm first detects seizure in short, nonoverlapping segments. The binary decisions made by Stage-I as ictals are fed to Stage-II with the goal of reducing the false alert rate (FAR). A post-processing framework is applied to the segment-level binary results to create event-level decisions.

Results: The performance of the two-stage system for detecting electrographically focal seizures was evaluated on EEGs recorded in a multi-center study. The two-stage algorithm exhibited increased sensitivity and reduced FAR when compared to singlestage models. For example, a two-stage model employing a balanced bagging classifier for Stage-I and a gradient boosting classifier for Stage-II improved the sensitivity of seizure detection from 61 $boldsymbol{pm }$ 5.9% to 75 $boldsymbol{pm }$ 6.6% while reducing the FAR from 3.3 $boldsymbol{pm }$ 0.3/hr to 2.4 $boldsymbol{pm }$ 0.3/hr.

Conclusion: The two-stage algorithm of this paper exhibited statistically significant performance improvement in detecting electrographically focal seizures over single-stage approaches. In addition, adding memory at the input of Stage-I and incorporating an iterative learning algorithm in Stage-I statistically significantly improved the performance of the first stage.

Significance: The performance of the two-stage method for single-channel seizure detection suggests its potential to enhance support systems used by epileptologists for post-hoc reviews. This system may represent the beginning of the roadmap for long-duration seizure monitoring using wearable single-channel EEG sensors during activities of daily life.