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.

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network
AbstractPresently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity –based graph convolutional network (MFC-GCN) framework is proposed, using not only full b...
Source: Medical and Biological Engineering and Computing - March 8, 2024 Category: Biomedical Engineering Source Type: research

Direct lingam and visibility graphs for analyzing brain connectivity in BCI
In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.Graphical abstract (Source: Medical and Biological Engineering and Computing)
Source: Medical and Biological Engineering and Computing - March 8, 2024 Category: Biomedical Engineering Source Type: research

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels
AbstractThe pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully ut...
Source: Medical and Biological Engineering and Computing - March 8, 2024 Category: Biomedical Engineering Source Type: research

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort
This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013 –2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, bo dy mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the bes...
Source: Medical and Biological Engineering and Computing - March 7, 2024 Category: Biomedical Engineering Source Type: research

Custom orthotic design by integrating 3D scanning and subject-specific FE modelling workflow
This study aimed to develop a subject-specific scaled foot modelling workflow for the foot orthoses design based on the scanned foot surface data. Six participants (twelve feet) were collected for the foot finite element modelling. The subject-specific surface-based finite element model (SFEM) was established by incorporating the scanned foot surface and scaled foot bone geometries. The geometric deviations between the scaled and the scanned foot surfaces were calculated. The SFEM model was adopted to predict barefoot and foot-orthosis interface pressures. The averaged distances between the scaled and scanned foot surfaces...
Source: Medical and Biological Engineering and Computing - March 6, 2024 Category: Biomedical Engineering Source Type: research

Advances and challenges in organ-on-chip technology: toward mimicking human physiology and disease in vitro
AbstractOrgans-on-chips have been tissues or three-dimensional (3D) mini-organs that comprise numerous cell types and have been produced on microfluidic chips to imitate the complicated structures and interactions of diverse cell types and organs under controlled circumstances. Several morphological and physiological distinctions exist between traditional 2D cultures, animal models, and the growing popular 3D cultures. On the other hand, animal models might not accurately simulate human toxicity because of physiological variations and interspecies metabolic capability. The on-chip technique allows for observing and underst...
Source: Medical and Biological Engineering and Computing - March 4, 2024 Category: Biomedical Engineering Source Type: research

Echoes of images: multi-loss network for image retrieval in vision transformers
AbstractThis paper introduces a novel approach to enhance content-based image retrieval, validated on two benchmark datasets: ISIC-2017 and ISIC-2018. These datasets comprise skin lesion images that are crucial for innovations in skin cancer diagnosis and treatment. We advocate the use of pre-trained Vision Transformer (ViT), a relatively uncharted concept in the realm of image retrieval, particularly in medical scenarios. In contrast to the traditionally employed Convolutional Neural Networks (CNNs), our findings suggest that ViT offers a more comprehensive understanding of the image context, essential in medical imaging....
Source: Medical and Biological Engineering and Computing - March 4, 2024 Category: Biomedical Engineering Source Type: research

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG
In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA ’s Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-bas ed three-level classification accuracy ranged from 85.4 to 97.4% (p <  0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. Thet-t...
Source: Medical and Biological Engineering and Computing - March 3, 2024 Category: Biomedical Engineering Source Type: research

Influence of build orientation and support structure on additive manufacturing of human knee replacements: a computational study
This article presents a computational framework that uses CT images to create patient-specific finite element models for optimizing AM knee replacements. The workflow includes image processing in the open-source software 3DSlicer and MeshLab and AM process simulations in the commercial platform 3DEXPERIENCE. The approach is demonstrated on a distal femur replacement for a 50-year-old male patient from the open-access Natural Knee Data. The results show that build orientations have a significant impact on both shape distortions and residual stresses. Support structures have a marginal effect on residual stresses but strongl...
Source: Medical and Biological Engineering and Computing - March 3, 2024 Category: Biomedical Engineering Source Type: research

LGDNet: local feature coupling global representations network for pulmonary nodules detection
AbstractDetection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing this task. However, existing deep convolutional neural networks exhibit limited long-range dependency capabilities and neglect crucial contextual information, resulting in reduced performance on detecting small-size nodules in CT scans. In this work, we propose a novel end-to-end framework called LGDNet for the detection of suspicious pulmonary nodules in lung CT scans...
Source: Medical and Biological Engineering and Computing - March 2, 2024 Category: Biomedical Engineering Source Type: research

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography
AbstractChallenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 u...
Source: Medical and Biological Engineering and Computing - February 20, 2024 Category: Biomedical Engineering Source Type: research

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals
AbstractIn recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on traditional deep learning methods may led to the loss of some useful information. This paper proposed a novel\(\uptau\)-shaped convolutional network (\(\mathrm{\tau Net}\)) aiming to address this issue. Unlike traditional network structures,\(\mathrm{\tau Net}\) incorporates the operations of upsampling features and concatenating high- and low-level features, enabling full u...
Source: Medical and Biological Engineering and Computing - February 20, 2024 Category: Biomedical Engineering Source Type: research

Deep Upscale U-Net for automatic tongue segmentation
AbstractIn a treatment or diagnosis related to oral health conditions such as oral cancer and oropharyngeal cancer, an investigation of tongue ’s movements is a major part. In an automatic measurement of such movement, it must first start with a task of tongue segmentation. This paper proposes a solution of tongue segmentation based on a decoder-encoder CNN-based structure i.e., U-Net. However, it could suffer from a problem of feature l oss in deep layers. This paper proposes a Deep Upscale U-Net (DU-UNET). An additional up-sampling of the feature map from a contracting path is concatenated to an upper layer of an expan...
Source: Medical and Biological Engineering and Computing - February 19, 2024 Category: Biomedical Engineering Source Type: research

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images
AbstractChronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the\(1,781\times 2\) lung radiomics and\(13,824\times 2\) 3D CNN features. Furthermore...
Source: Medical and Biological Engineering and Computing - February 16, 2024 Category: Biomedical Engineering Source Type: research

A conformal regressor for predicting negative conversion time of Omicron patients
AbstractIn light of the situation and the characteristics of Omicron, the country has continuously optimized the rules for the prevention and control of COVID-19. The global epidemic is still spreading, and new cases of infection continue to emerge in China. To facilitate the infected person to estimate the course of virus infection, a prediction model for predicting negative conversion time is proposed in this article. The clinical features of Omicron-infected patients in Shandong Province in the first half of 2022 are retrospectively studied. These features are grouped by disease diagnosis result, clinical sign, traditio...
Source: Medical and Biological Engineering and Computing - February 16, 2024 Category: Biomedical Engineering Source Type: research