Table of Contents
null (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - May 2, 2024 Category: Biomedical Engineering Source Type: research
Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks
AbstractAccurate determination of body segment parameters is crucial for studying human movement and joint forces using musculoskeletal (MSK) models. However, existing methods for predicting segment mass have limited generalizability and sensitivity to body shapes. With recent advancements in machine learning, this study proposed a novel artificial neural network-based method for computing subject-specific trunk segment mass and center of mass (CoM) using only anthropometric measurements. We first developed, trained, and validated two artificial neural networks that used anthropometric measurements as input to predict body...
Source: Medical and Biological Engineering and Computing - May 2, 2024 Category: Biomedical Engineering Source Type: research
BraNet: a mobil application for breast image classification based on deep learning algorithms
This study aims to develop an open-source mobile app named “BraNet” for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client–server architecture and ...
Source: Medical and Biological Engineering and Computing - May 2, 2024 Category: Biomedical Engineering Source Type: research
Adversarial attacks and adversarial training for burn image segmentation based on deep learning
AbstractDeep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the model ’s results in image segmentation and classification. Especially in medical images, due to constraints from factors like shooting angles, environmental lighting, and diverse photography devices, medical images typically contain various forms of noise. In order to address the impact of these physica lly meaningful disturbances on existing deep learning models in the application of burn image segmentation, we simulate attack methods inspired by natural phenomena and propose an ...
Source: Medical and Biological Engineering and Computing - May 2, 2024 Category: Biomedical Engineering Source Type: research
Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs
AbstractTo predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 wit...
Source: Australasian Physical and Engineering Sciences in Medicine - May 2, 2024 Category: Biomedical Engineering Source Type: research
Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI
Ann Biomed Eng. 2024 May 1. doi: 10.1007/s10439-024-03510-3. Online ahead of print.ABSTRACTParotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance images is essential in accurately diagnosing and selecting appropriate surgical plans. However, segmentation of parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently, deep learning has developed rapidly, and Transformer-based networks have performed well on many computer vision tasks. However, Transformer-based networks...
Source: Annals of Biomedical Engineering - May 1, 2024 Category: Biomedical Engineering Authors: Zi'an Xu Yin Dai Fayu Liu Siqi Li Sheng Liu Lifu Shi Jun Fu Source Type: research
Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI
Ann Biomed Eng. 2024 May 1. doi: 10.1007/s10439-024-03510-3. Online ahead of print.ABSTRACTParotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance images is essential in accurately diagnosing and selecting appropriate surgical plans. However, segmentation of parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently, deep learning has developed rapidly, and Transformer-based networks have performed well on many computer vision tasks. However, Transformer-based networks...
Source: Annals of Biomedical Engineering - May 1, 2024 Category: Biomedical Engineering Authors: Zi'an Xu Yin Dai Fayu Liu Siqi Li Sheng Liu Lifu Shi Jun Fu Source Type: research
Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI
Ann Biomed Eng. 2024 May 1. doi: 10.1007/s10439-024-03510-3. Online ahead of print.ABSTRACTParotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance images is essential in accurately diagnosing and selecting appropriate surgical plans. However, segmentation of parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently, deep learning has developed rapidly, and Transformer-based networks have performed well on many computer vision tasks. However, Transformer-based networks...
Source: Annals of Biomedical Engineering - May 1, 2024 Category: Biomedical Engineering Authors: Zi'an Xu Yin Dai Fayu Liu Siqi Li Sheng Liu Lifu Shi Jun Fu Source Type: research
Chinese guidelines for the diagnosis and management of atrial fibrillation
AbstractAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia, significantly impacting patients' quality of life and increasing the risk of death, stroke, heart failure, and dementia. Over the past two decades, there have been significant breakthroughs in AF risk prediction and screening, stroke prevention, rhythm control, catheter ablation, and integrated management. During this period, the scale, quality, and experience of AF management in China have greatly improved, providing a solid foundation for the development of the guidelines for the diagnosis and management of AF. To further promote standardiz...
Source: Pacing and Clinical Electrophysiology : PACE - May 1, 2024 Category: Cardiology Authors: Changsheng Ma,
Shulin Wu,
Shaowen Liu,
Yaling Han Tags: ELECTROPHYSIOLOGY Source Type: research
Efficient skin lesion segmentation with boundary distillation
AbstractMedical image segmentation models are commonly known for their complex structures, which often render them impractical for use on edge computing devices and compromising efficiency in the segmentation process. In light of this, the industry has proposed the adoption of knowledge distillation techniques. Nevertheless, the vast majority of existing knowledge distillation methods are focused on the classification tasks of skin diseases. Specifically, for the segmentation tasks of dermoscopy lesion images, these knowledge distillation methods fail to fully recognize the importance of features in the boundary regions of...
Source: Medical and Biological Engineering and Computing - May 1, 2024 Category: Biomedical Engineering Source Type: research
Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach
Comput Methods Biomech Biomed Engin. 2024 Apr 30:1-14. doi: 10.1080/10255842.2024.2346356. Online ahead of print.ABSTRACTThe successful implementation of neural network-based EEG signal compression has led to significant cost reductions in data transmission. However, a major obstacle in this process arises from the decline in performance when compressing EEG signals from multiple subjects. This challenge arises due to the notable feature shift of EEG signals between subjects, which poses an impediment to the neural network's efficient concurrent acquisition of information from multiple subjects. To address this limitation ...
Source: Computer Methods in Biomechanics and Biomedical Engineering - April 30, 2024 Category: Biomedical Engineering Authors: Yongfei Liu Fan Yang Binbin Wu Source Type: research
Dual stress factors adaptive evolution for high EPA production in Schizochytrium sp. and metabolomics mechanism analysis
This study provides a novel pathway for promoting EPA biosynthesis in Schizochytrium.PMID:38687387 | DOI:10.1007/s00449-024-03013-4 (Source: Bioprocess and Biosystems Engineering)
Source: Bioprocess and Biosystems Engineering - April 30, 2024 Category: Biomedical Engineering Authors: Ying Ou Yu Qin Shoushuai Feng Hailin Yang Source Type: research
TriKSV-LG: a robust approach to disease prediction in healthcare systems using AI and Levy Gazelle optimization
Comput Methods Biomech Biomed Engin. 2024 Apr 30:1-17. doi: 10.1080/10255842.2024.2339479. Online ahead of print.ABSTRACTA seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vect...
Source: Computer Methods in Biomechanics and Biomedical Engineering - April 30, 2024 Category: Biomedical Engineering Authors: Kavitha Dhanushkodi Prema Vinayagasundaram Vidhya Anbalagan Surendran Subbaraj Ravikumar Sethuraman Source Type: research
Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach
Comput Methods Biomech Biomed Engin. 2024 Apr 30:1-14. doi: 10.1080/10255842.2024.2346356. Online ahead of print.ABSTRACTThe successful implementation of neural network-based EEG signal compression has led to significant cost reductions in data transmission. However, a major obstacle in this process arises from the decline in performance when compressing EEG signals from multiple subjects. This challenge arises due to the notable feature shift of EEG signals between subjects, which poses an impediment to the neural network's efficient concurrent acquisition of information from multiple subjects. To address this limitation ...
Source: Computer Methods in Biomechanics and Biomedical Engineering - April 30, 2024 Category: Biomedical Engineering Authors: Yongfei Liu Fan Yang Binbin Wu Source Type: research
Dual stress factors adaptive evolution for high EPA production in Schizochytrium sp. and metabolomics mechanism analysis
This study provides a novel pathway for promoting EPA biosynthesis in Schizochytrium.PMID:38687387 | DOI:10.1007/s00449-024-03013-4 (Source: Bioprocess and Biosystems Engineering)
Source: Bioprocess and Biosystems Engineering - April 30, 2024 Category: Biomedical Engineering Authors: Ying Ou Yu Qin Shoushuai Feng Hailin Yang Source Type: research
