生物医学工程学杂志最新文献

Prostate cancer is one of the most prevalent malignancies among men worldwide, and its diagnosis relies heavily on accurate analysis of whole slide imaging (WSI) in histopathology. However, manual interpretation is time-consuming and prone to inconsistent accuracy. Existing multiple instance learning (MIL)-based studies can assist diagnosis but still suffer from high computational cost, insufficient exploitation of inter-instance relationships, and neglect of tissue heterogeneity. To address these challenges, this paper proposes a feature distillation multiple instance learning method based on sequence reorganization mamba (FDMIL). The proposed approach leveraged the long-sequence modeling capability of SR-Mamba to capture effective inter-instance dependencies and heterogeneity. Meanwhile, a feature distillation mechanism was introduced to remove redundant representations and reduce computational overhead. Additionally, an auxiliary loss function was designed to mitigate pseudo-bag noise interference. We evaluated FDMIL on the Peking Union Medical College Hospital (PUMCH) prostate cancer WSI dataset and the public Camelyon16 dataset. Experimental results demonstrated that FDMIL achieved significant performance improvements on both datasets, reaching an AUC of 93.9%, ACC of 90.1%, and F1-score of 87.3%, outperforming existing state-of-the-art methods. These results verify the effectiveness and clinical applicability of FDMIL in both institutional and public scenarios.

Intraspinal microstimulation (ISMS) is a rehabilitation technology that activates muscle movement by electrically stimulating the spinal cord, thereby restoring the function of paralyzed limbs. In this study, a fuzzy logic-controlled self-tuning proportional-integral-derivative (PID) algorithm was adopted. By simultaneously adjusting three key electrical stimulation parameters-amplitude, pulse width, and frequency of the pulse signal-the distal locomotor central pattern generator (CPG) in rats with spinal cord injury (SCI) was activated, realizing real-time control of hindlimb ankle joint movement in paralyzed rats. To verify the control performance of the intraspinal microstimulation system, animal experiments were conducted. Statistical results showed that the root mean square error (RMSE) of joint angle tracking was 2.50°, and the normalized root mean square error (NRMSE) was 5.78%. The results indicate that the ankle joint of the paralyzed hindlimb in SCI rats can move according to the preset angle trajectory through single-electrode intraspinal electrical stimulation.

This study aims to investigate the impact of tibial tray fixation peg structure in posterior stabilized (PS) knee prostheses on its initial fixation stability, a finite element model and a micromotion prediction model of PS total knee arthroplasty (TKA) were established to comparatively study the differences in the von Mises stress of the proximal tibia and the micromotion at the bone-prosthesis fixation interface under four PS tibial tray fixation peg design, namely cylindrical plus hemispherical, cylindrical plus conical, hexagonal prism, and cruciform. The results showed that, at the moment of the maximum force of knee joint during level walking activity, there was no significant difference in the tibial von Mises stress between the tibial tray with or without fixation peg designs. However, the peak micromotions at the prosthesis fixation interface of all tibial trays with fixation peg design were significantly reduced. Among them, the micromotion suppression effect of the cruciform fixation peg was the most obvious. At the moment of the maximum flexion angle of knee joint during squatting activity, the tibial von Mises stress for tibial trays with fixation peg design was clearly lower than that without fixation peg design, meanwhile the peak micromotion at the prosthesis fixation interface was also significantly reduced. Overall, the cruciform fixation peg design showed the best fixation stability and effectively reduced the loosening risk at the prosthesis fixation interface. This study recommended that the backside of the tibial tray in non-cemented PS knee prostheses adopted a design combining a cylindrical stem with a serrated keel and a cruciform fixation peg. This study provided an important reference basis for improving the initial fixation stability of non-cemented PS knee prostheses by optimizing the backside design of the tibial tray.

Cross-modal unsupervised domain adaptation (UDA) aims to transfer segmentation models trained on a labeled source modality to an unlabeled target modality. However, existing methods often fail to fully exploit shape priors and intermediate feature representations, resulting in limited generalization ability of the model in cross-modal transfer tasks. To address this challenge, we propose a segmentation model based on shape-aware adaptive weighting (SAWS) that enhance the model's ability to perceive the target area and capture global and local information. Specifically, we design a multi-angle strip-shaped shape perception (MSSP) module that captures shape features from multiple orientations through an angular pooling strategy, improving structural modeling under cross-modal settings. In addition, an adaptive weighted hierarchical contrastive (AWHC) loss is introduced to fully leverage intermediate features and enhance segmentation accuracy for small target structures. The proposed method is evaluated on the multi-modality whole heart segmentation (MMWHS) dataset. Experimental results demonstrate that SAWS achieves superior performance in cross-modal cardiac segmentation tasks, with a Dice score (Dice) of 70.1% and an average symmetric surface distance (ASSD) of 4.0 for the computed tomography (CT)→magnetic resonance imaging (MRI) task, and a Dice of 83.8% and ASSD of 3.7 for the MRI→CT task, outperforming existing state-of-the-art methods. Overall, this study proposes a cross-modal medical image segmentation method with shape-aware, which effectively improves the structure-aware ability and generalization performance of the UDA model.

Current epilepsy prediction methods are not effective in characterizing the multi-domain features of complex long-term electroencephalogram (EEG) data, leading to suboptimal prediction performance. Therefore, this paper proposes a novel multi-scale sparse adaptive convolutional network based on multi-head attention mechanism (MS-SACN-MM) model to effectively characterize the multi-domain features. The model first preprocesses the EEG data, constructs multiple convolutional layers to effectively avoid information overload, and uses a multi-layer perceptron and multi-head attention mechanism to focus the network on critical pre-seizure features. Then, it adopts a focal loss training strategy to alleviate class imbalance and enhance the model's robustness. Experimental results show that on the publicly created dataset (CHB-MIT) by MIT and Boston Children's Hospital, the MS-SACN-MM model achieves a maximum accuracy of 0.999 for seizure prediction 10 ~ 15 minutes in advance. This demonstrates good predictive performance and holds significant importance for early intervention and intelligent clinical management of epilepsy patients.

With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.

Pan-vascular diseases encompass a range of systemic conditions characterized by sharing a common pathologic basis of vascular deterioration. Due to the complexity of these diseases, a thorough understanding on their similarities and differences is essential for optimizing diagnosis and treatment strategies. Magnetic resonance imaging (MRI), as one of the commonly used medical imaging techniques, has been widely applied in the diagnosis of pan-vascular diseases. Particularly, the integration of MRI with contrast agents and multi-parameter imaging techniques significantly enhances diagnostic accuracy, reducing the likelihood of missed or incorrect diagnoses. Recently, a variety of nano-magnetic resonance contrast agents have been developed and applied to the magnetic resonance imaging diagnosis of diseases. These nanotechnology-based contrast agents provide multiple advantages, ensuring more precise and forward-looking imaging of pan-vascular conditions. In this review, the diverse application strategies of nanomaterials-enhanced MRI techniques in the diagnosis of pan-vascular diseases were systematically summarized, by classifying them into the commonly used MRI sequences in clinical practice. Additionally, the potential advantages and challenges associated with the clinical translation of nanomaterial-enhanced MRI were also discussed. This review not only offers a novel perspective on the precise diagnosis of pan-vascular diseases, but also serves as a valuable reference for future clinical practice and research in the field.

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.

In animal navigation, head direction is encoded by head direction cells within the olfactory-hippocampal structures of the brain. Even in darkness or unfamiliar environments, animals can estimate their head direction by integrating self-motion cues, though this process accumulates errors over time and undermines navigational accuracy. Traditional strategies rely on visual input to correct head direction, but visual scenes combined with self-motion information offer only partially accurate estimates. This study proposed an innovative calibration mechanism that dynamically adjusts the association between visual scenes and head direction based on the historical firing rates of head direction cells, without relying on specific landmarks. It also introduced a method to fine-tune error correction by modulating the strength of self-motion input to control the movement speed of the head direction cell activity bump. Experimental results showed that this approach effectively reduced the accumulation of self-motion-related errors and significantly enhanced the accuracy and robustness of the navigation system. These findings offer a new perspective for biologically inspired robotic navigation systems and underscore the potential of neural mechanisms in enabling efficient and reliable autonomous navigation.

Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.