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Mechanobiochemical bone remodelling around an uncemented acetabular component: influence of bone orthotropy
This study aims to propose a novel framework of bone remodelling based on the combined effects of bone orthotropy and mechanobiochemical stimulus. The proposed remodelling framework was employed in the finite element model of an implanted hemipelvis to predict evolutionary changes in bone density and associated orthotropic bone material properties. In order to account for variations in load transfer during common daily activities, several musculoskeletal loading conditions of hip joint corresponding to sitting down/up, stairs ascend/descend and normal walking were considered. The bone remodelling predictions were compared ...
Source: Medical and Biological Engineering and Computing - February 14, 2024 Category: Biomedical Engineering Source Type: research

Meta-lasso: new insight on infection prediction after minimally invasive surgery
AbstractSurgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in a...
Source: Medical and Biological Engineering and Computing - February 13, 2024 Category: Biomedical Engineering Source Type: research

Three-stage transfer learning for motor imagery EEG recognition
Abstract   Motor imagery (MI) paradigms have been widely used in neural rehabilitation and drowsiness state assessment. The progress in brain-computer interface (BCI) technology has emphasized the importance of accurately and efficiently detecting motor imagery intentions from electroencephalogram (EEG). Despite the recent breakthroughs made in developing EEG-based algorithms for decoding MI, the accuracy and efficiency of these models remain limited by technical challenges posed by cross-subject heterogeneity in EEG data processing and the scarcity of EEG data for training. Inspired by the optimal transport theory, this...
Source: Medical and Biological Engineering and Computing - February 12, 2024 Category: Biomedical Engineering Source Type: research

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation
AbstractEarly intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Opti...
Source: Medical and Biological Engineering and Computing - February 8, 2024 Category: Biomedical Engineering Source Type: research

Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification
AbstractElectroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects ’ motor imagery activities; this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usually time-consuming and labor-intensive, which makes it difficult to obtain sufficient labeled data from the new subject to tr ain a new model. Moreover, the EEG signals of different individuals exhibit significant differences, leading to a significant drop in the performance of a model trained on the existing subjects...
Source: Medical and Biological Engineering and Computing - February 7, 2024 Category: Biomedical Engineering Source Type: research

A macro –micro FE and ANN framework to assess site-specific bone ingrowth around the porous beaded-coated implant: an example with BOX® tibial implant for total ankle replacement
AbstractThe use of mechanoregulatory schemes based on finite element (FE) analysis for the evaluation of bone ingrowth around porous surfaces is a viable approach but requires significant computational time and effort. The aim of this study is to develop a combined macro –micro FE and artificial neural network (ANN) framework for rapid and accurate prediction of the site-specific bone ingrowth around the porous beaded-coated tibial implant for total ankle replacement (TAR). A macroscale FE model of the implanted tibia was developed based on CT data. Subsequently, a microscale FE model of the implant-bone interface was cr...
Source: Medical and Biological Engineering and Computing - February 7, 2024 Category: Biomedical Engineering Source Type: research

Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism
AbstractThis paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global recepti...
Source: Medical and Biological Engineering and Computing - February 6, 2024 Category: Biomedical Engineering Source Type: research

Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes
AbstractInvasive gene expression profiling studies have exposed prognostically significant breast cancer subtypes: normal-like, luminal, HER-2 enriched, and basal-like, which is defined in large part by human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER). However, while dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been generally employed in the screening and therapy of breast cancer, there is a challenging problem to noninvasively predict breast cancer molecular subtypes, which have extremely low-data regimes. In this paper, a novel few-shot learnin...
Source: Medical and Biological Engineering and Computing - February 6, 2024 Category: Biomedical Engineering Source Type: research

LieWaves: dataset for lie detection based on EEG signals and wavelets
This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep lea...
Source: Medical and Biological Engineering and Computing - February 5, 2024 Category: Biomedical Engineering Source Type: research

GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation
AbstractThe ongoing COronaVIrus Disease 2019 (COVID-19) pandemic carried by the SARS-CoV-2 virus spread worldwide in early 2019, bringing about an existential health catastrophe. Automatic segmentation of infected lungs from COVID-19 X-ray and computer tomography (CT) images helps to generate a quantitative approach for treatment and diagnosis. The multi-class information about the infected lung is often obtained from the patient ’s CT dataset. However, the main challenge is the extensive range of infected features and lack of contrast between infected and normal areas. To resolve these issues, a novel Global Infection F...
Source: Medical and Biological Engineering and Computing - February 3, 2024 Category: Biomedical Engineering Source Type: research

Computer simulation-based nanothermal field and tissue damage analysis for cardiac tumor ablation
In this study, we are acquainted with a nanoassisted RF ablation procedure of cardiac tumor to provide better outcomes for long-term survival rate without any recurrences. A three-dimensional thermo-electric energy model is employed to investigate nanothermal field and ablation efficiency into the left atrium tumor. The cell death model is adopted to quantify the degree of tissue injury while injecting the Fe3O4 nanoparticles concentrations up to 20% into the target tissue. The results reveal that when nanothermal field extents as a function of tissue depth (10  mm) from the electrode tip, the increasing thermal rates wer...
Source: Medical and Biological Engineering and Computing - February 3, 2024 Category: Biomedical Engineering Source Type: research

A survey on the state of the art of force myography technique (FMG): analysis and assessment
AbstractPrecise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low comput...
Source: Medical and Biological Engineering and Computing - February 2, 2024 Category: Biomedical Engineering Source Type: research

A multimodal virtual vision platform as a next-generation vision system for a surgical robot
Abstract   Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional...
Source: Medical and Biological Engineering and Computing - February 2, 2024 Category: Biomedical Engineering Source Type: research

A lightweight bladder tumor segmentation method based on attention mechanism
AbstractIn the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors ’ diagnosis and patients’ later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM appl ies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scal...
Source: Medical and Biological Engineering and Computing - February 2, 2024 Category: Biomedical Engineering Source Type: research

Cancer detection and classification using a simplified binary state vector machine
This study presents an efficient machine learning approach based on the state vector machine (SVM) to diagnose and classify tumors into malignant or benign cancer using the online lymphographic data. Further, two types of neural network architectures are also implemented to evaluate the performance of the proposed SVM-based approach. The optimal structures of the classifiers are obtained by varying the architecture, topology, learning rate, and kernel function and recording the results ’ accuracy. The classifiers are trained with the preprocessed data examples after noise removal and tested on the unknown cases to diagno...
Source: Medical and Biological Engineering and Computing - February 1, 2024 Category: Biomedical Engineering Source Type: research