生物医学工程学杂志最新文献

In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.

To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [-4.78, 4.78] beats per minute, and a consistency error of -0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.

Slow wound healing has been a troublesome problem in clinic. In China, traditional methods such as antibiotics and silver sulfadiazine are used to treat skin wound, but the abuse use has many disadvantages, such as chronic wounds and pathogen resistance. Studies have shown that the microorganisms with symbiotic relationship with organisms have benefits on skin wound. Therefore, the way to develop and utilize probiotics to promote wound healing has become a new research direction. In this paper, we reviewed the studies on the bacteriotherapy in the world, described how the probiotics can play a role in promoting wound healing through local wound and intestine, and introduced some mature probiotics products and clinical trials, aiming to provide foundations for further development of bacteriotherapy and products.

Joint attention deficit is one of the core disorders in children with autism, which seriously affects the development of multiple basic skills such as language and communication. Virtual reality scene intervention has great potential in improving joint attention skills in children with autism due to its good interactivity and immersion. This article reviewed the application of virtual reality based social and nonsocial scenarios in training joint attention skills for children with autism in recent years, summarized the problems and challenges of this intervention method, and proposed a new joint paradigm for social scenario assessment and nonsocial scenario training. Finally, it looked forward to the future development and application prospects of virtual reality technology in joint attention skill training for children with autism.

Tumor treatment fields (TTFields) can effectively inhibit the proliferation of tumor cells, but its mechanism remains exclusive. The destruction of cellular microtubule structure caused by TTFields through electric field force is considered to be the main reason for inhibiting tumor cell proliferation. However, the validity of this hypothesis still lacks exploration at the mesoscopic level. Therefore, in this study, we built force models for tubulins subjected to TTFields, based on the physical and electrical properties of tubulin molecules. We theoretically analyzed and simulated the dynamic effects of electric field force and torque on tubulin monomer polymerization, as well as the alignment and orientation of α/β tubulin heterodimer, respectively. Research results indicate that the interference of electric field force induced by TTFields on tubulin monomer is notably weaker than the inherent electrostatic binding force among tubulin monomers. Additionally, the electric field torque generated by the TTFileds on α/β tubulin dimers is also difficult to affect their random alignment. Therefore, at the mesoscale, our study affirms that TTFields are improbable to destabilize cellular microtubule structures via electric field dynamics effects. These results challenge the traditional view that TTFields destroy the microtubule structure of cells through TTFields electric field force, and proposes a new approach that should pay more attention to the "non-mechanical" effects of TTFields in the study of TTFields mechanism. This study can provide reliable theoretical basis and inspire new research directions for revealing the mesoscopic bioelectrical mechanism of TTFields.

Near-infrared fluorescence imaging technology, which possesses superior advantages including real-time and fast imaging, high spatial and temporal resolution, and deep tissue penetration, shows great potential for tumor imaging in vivo and therapy. Ⅰ-Ⅲ-Ⅵ quantum dots exhibit high brightness, broad excitation, easily tunable emission wavelength and superior stability, and do not contain highly toxic heavy metal elements such as cadmium or lead. These advantages make Ⅰ-Ⅲ-Ⅵ quantum dots attract widespread attention in biomedical field. This review summarizes the recent advances in the controlled synthesis of Ⅰ-Ⅲ-Ⅵ quantum dots and their applications in tumor imaging in vivo and therapy. Firstly, the organic-phase and aqueous-phase synthesis of Ⅰ-Ⅲ-Ⅵ quantum dots as well as the strategies for regulating the near-infrared photoluminescence are briefly introduced; secondly, representative biomedical applications of near-infrared-emitting cadmium-free quantum dots including early diagnosis of tumor, lymphatic imaging, drug delivery, photothermal and photodynamic therapy are emphatically discussed; lastly, perspectives on the future directions of developing quantum dots for biomedical application and the faced challenges are discussed. This paper may provide guidance and reference for further research and clinical translation of cadmium-free quantum dots in tumor diagnosis and treatment.

Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of "visual attention" crucial, but the modeling of "question attention" is equally significant. To facilitate bidirectional reasoning in the attention processes involving medical images and questions, a new MVQA architecture, named MCAN, has been proposed. This architecture incorporated a cross-modal co-attention network, FCAF, which identifies key words in questions and principal parts in images. Through a meta-learning channel attention module (MLCA), weights were adaptively assigned to each word and region, reflecting the model's focus on specific words and regions during reasoning. Additionally, this study specially designed and developed a medical domain-specific word embedding model, Med-GloVe, to further enhance the model's accuracy and practical value. Experimental results indicated that MCAN proposed in this study improved the accuracy by 7.7% on free-form questions in the Path-VQA dataset, and by 4.4% on closed-form questions in the VQA-RAD dataset, which effectively improves the accuracy of the medical vision question answer.