Medical and Biological Engineering and Computing Medical and Biological Engineering and Computing RSS feedThis is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Design and analysis of a compatible exoskeleton rehabilitation robot system based on upper limb movement mechanism
AbstractRehabilitation robots are used to promote structural and functional recovery of the nervous system with repetitive, task-oriented training and have been gradually applied to clinical rehabilitation training. This paper proposes an upper limb exoskeleton rehabilitation robot system that could realize shoulder-elbow-wrist joint rehabilitation training. Firstly, a motion equivalent model was established based on the upper limb movement mechanism, the robot mechanism configuration was designed, and the optimization algorithm and spatial mechanism theory were used to optimize and analyze the structural parameters and hu...
Source: Medical and Biological Engineering and Computing - December 12, 2023 Category: Biomedical Engineering Source Type: research

Magnetic resonance imaging and deoxyribonucleic acid methylation –based radiogenomic models for survival risk stratification of glioblastoma
This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes ...
Source: Medical and Biological Engineering and Computing - December 7, 2023 Category: Biomedical Engineering Source Type: research

Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity
AbstractRetinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment of the retinal vascular tortuosity from color fundus images. Our methodology takes into consideration several anatomical factors to weigh the importance of each individual blood vessel. First, we use deep neural networks to produce a robust extraction of the different anatomical structures. Then, the weighting coefficients that are required for the integration of the different anatomical factors are adjuste...
Source: Medical and Biological Engineering and Computing - December 7, 2023 Category: Biomedical Engineering Source Type: research

Biomedical analysis of four fixation systems in treatment of type II traumatic spondylolisthesis of the axis: a finite element analysis
This study aimed to compare the properties and safety of self-designed plates in type II traumatic spondylolisthesis of the axis with those of traditional devices via finite element (FE) analysis. We constructed a hangman ’s fracture FE model from the occipital bone (C0) level to the C3 level. Then, FE models were constructed for the following four fixation systems: an anterior cervical L-shaped plate with four vertebral screws (4-ACLP), or six screws (6-ACLP), an anterior cervical orion plate (ACOP), and a posteri or fixation system. A preloaded compressive force of 50 N and a moment of 1.5 N·m were applied to each mod...
Source: Medical and Biological Engineering and Computing - December 6, 2023 Category: Biomedical Engineering Source Type: research

A comparative study of accuracy in major adaptive filters for motion artifact removal in sleep apnea tests
This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters.Graphical abstract...
Source: Medical and Biological Engineering and Computing - December 5, 2023 Category: Biomedical Engineering Source Type: research

Repetitive transcranial magnetic stimulation (rTMS) as a tool for cognitive enhancement in healthy adults: a review study
AbstractAs human beings, we have always sought to expand on our abilities, including our cognitive and motor skills. One of the still-underrated tools employed to this end is repetitive transcranial magnetic stimulation (rTMS). Until recently, rTMS was almost exclusively used in studies with rehabilitation purposes. Only a small strand of literature has focused on the application of rTMS on healthy people with the aim of enhancing cognitive abilities such as decision-making, working memory, attention, source memory, cognitive control, learning, computational speed, risk-taking, and impulsive behaviors. It, therefore, seems...
Source: Medical and Biological Engineering and Computing - December 4, 2023 Category: Biomedical Engineering Source Type: research

SCAN: sequence-based context-aware association network for hepatic vessel segmentation
AbstractAccurate segmentation of hepatic vessel is significant for the surgeons to design the preoperative planning of liver surgery. In this paper, a sequence-based context-aware association network (SCAN) is designed for hepatic vessel segmentation, in which three schemes are incorporated to simultaneously extract the 2D features of hepatic vessels and capture the correlations between adjacent CT slices. The two schemes of slice-level attention module and graph association module are designed to bridge feature gaps between the encoder and the decoder in the low- and high-dimensional spaces. The region-edge constrained lo...
Source: Medical and Biological Engineering and Computing - November 30, 2023 Category: Biomedical Engineering Source Type: research

Multiphysics and multiscale modeling of uterine contractions: integrating electrical dynamics and soft tissue deformation with fiber orientation
In this study, we present a computational model that effectively integrates the electrical and mechanical components of uterine contractions. By combining a state-of-the-art electrical model with the Hyperelastic Mass-Spring Model (HyperMSM), we adopt a multiphysics and multiscale approach to capture the electrical and mechanical activities within the uterus. The electrical model incorporates the generation and propagation of action potentials, while the HyperMSM simulates the mechanical behavior and deformations of the uterine tissue. Notably, our model takes into account the orientation of muscle fibers, ensuring that th...
Source: Medical and Biological Engineering and Computing - November 26, 2023 Category: Biomedical Engineering Source Type: research

Brain fiber structure estimation based on principal component analysis and RINLM filter
In this study, the algorithm is applied to the fiber structure and the prevailing microstructural models within the human brain voxels based on simulated and real human brain datasets. Experimental comparisons between the proposed method and the state-of-the-art methods are performed in single-fiber, multi-fiber, crossed and curved-fiber regions as well as in different microstructure estimation models. Results demonstrated the superior performance of the proposed method in denoising DWI data, which can reduce the angular error in fiber orientation reconstruction to obtain more valid fiber structure estimation and enable mo...
Source: Medical and Biological Engineering and Computing - November 23, 2023 Category: Biomedical Engineering Source Type: research

An efficient multi-class classification of skin cancer using optimized vision transformer
AbstractSkin cancer is a pervasive and deadly disease, prompting a surge in research efforts towards utilizing computer-based techniques to analyze skin lesion images to identify malignancies. This paper introduces an optimized vision transformer approach for effectively classifying skin tumors. The methodology begins with a pre-processing step aimed at preserving color constancy, eliminating hair artifacts, and reducing image noise. Here, a combination of techniques such as piecewise linear bottom hat filtering, adaptive median filtering, Gaussian filtering, and an enhanced gradient intensity method is used for pre-proces...
Source: Medical and Biological Engineering and Computing - November 23, 2023 Category: Biomedical Engineering Source Type: research

In silico study about the influence of electroporation parameters on the cellular internalization, spatial uniformity, and cytotoxic effects of chemotherapeutic drugs using the Method of Fundamental Solutions
AbstractReversible electroporation is a suitable technique to aid the internalization of medicaments in cancer tissues without inducing permanent cellular damage, allowing the enhancement of cytotoxic effects without incurring in electric-driven necrotic or apoptotic processes by the presence of non-reversible aqueous pores. An adequate selection of electroporation parameters acquires relevance to reach these goals and avoid opposite effects. This work applies the Method of Fundamental Solutions (MFS) for drug transport simulations in electroporated cancer tissues, using a continuum tumor cord approach and considering both...
Source: Medical and Biological Engineering and Computing - November 21, 2023 Category: Biomedical Engineering Source Type: research

Re-UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction
This study may shed light on the reverse U-shaped network architecture for CT image reconstruction, and could investigate the potential on other medical image processing.Graphical abstract (Source: Medical and Biological Engineering and Computing)
Source: Medical and Biological Engineering and Computing - November 20, 2023 Category: Biomedical Engineering Source Type: research

EEGNet-based multi-source domain filter for BCI transfer learning
This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high ...
Source: Medical and Biological Engineering and Computing - November 20, 2023 Category: Biomedical Engineering Source Type: research

Identifying ADHD and subtypes through microstates analysis and complex networks
AbstractThe diagnosis of attention-deficit hyperactivity disorder (ADHD) is based on the health history and on the evaluation of questionnaires to identify symptoms. This evaluation can be subjective and lengthy, especially in children. Therefore, a biomarker would be of great value to assist mental health professionals in the process of diagnosing ADHD. Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes. Previous works suggested that specific sets of ERP-microstates are selectively affected by ADHD. This paper proposes a new methodology for the ERP-microstate...
Source: Medical and Biological Engineering and Computing - November 20, 2023 Category: Biomedical Engineering Source Type: research

mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI
This study proposes a brand new end-to-end model for brain tumor segmentation, which is a multi-scale deep residual convolutional neural network called mResU-Net. The semantic gap between the encoder and decoder is bridged by using skip connections in the U-Net structure. The residual structure is used to alleviate the vanishing gradient problem during training and ensure sufficient information in deep networks. On this basis, multi-scale convolution kernels are used to improve the segmentation accuracy of targets of different sizes. At the same time, we also integrate channel attention modules into the network to improve ...
Source: Medical and Biological Engineering and Computing - November 19, 2023 Category: Biomedical Engineering Source Type: research