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JOURNAL ARTICLE
Anthony Bozzo, James M G Tsui, Sahir Bhatnagar, Jonathan Forsberg
This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits...
April 17, 2024: Journal of the American Academy of Orthopaedic Surgeons
#2
JOURNAL ARTICLE
Dong Ouyang, Yong Liang, Jinfeng Wang, Le Li, Ning Ai, Junning Feng, Shanghui Lu, Shuilin Liao, Xiaoying Liu, Shengli Xie
Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation...
April 2024: PLoS Computational Biology
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JOURNAL ARTICLE
Licheng Jiao, Mengjiao Wang, Xu Liu, Lingling Li, Fang Liu, Zhixi Feng, Shuyuan Yang, Biao Hou
Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation...
April 23, 2024: IEEE Transactions on Neural Networks and Learning Systems
#4
JOURNAL ARTICLE
Christos Kyrkou
High-efficiency deep learning (DL) models are necessary not only to facilitate their use in devices with limited resources but also to improve resources required for training. Convolutional neural networks (ConvNets) typically exert severe demands on local device resources and this conventionally limits their adoption within mobile and embedded platforms. This brief presents work toward utilizing static convolutional filters generated from the space of local binary patterns (LBPs) and Haar features to design efficient ConvNet architectures...
April 23, 2024: IEEE Transactions on Neural Networks and Learning Systems
#5
JOURNAL ARTICLE
Jiawei Li, Yuanfei Deng, Yixiu Qin, Shun Mao, Yuncheng Jiang
Knowledge tracing (KT) refers to predicting learners' performance in the future according to their historical responses, which has become an essential task in intelligent tutoring systems. Most deep learning-based methods usually model the learners' knowledge states via recurrent neural networks (RNNs) or attention mechanisms. Recently emerging graph neural networks (GNNs) assist the KT model to capture the relationships such as question-skill and question-learner. However, non-pairwise and complex higher-order information among responses is ignored...
April 23, 2024: IEEE Transactions on Neural Networks and Learning Systems
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JOURNAL ARTICLE
Yi Ding, Su Zhang, Chuangao Tang, Cuntai Guan
Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG emotion recognition: continuous regression of emotional states and discrete classification of emotions. While classification methods have garnered significant attention, regression methods remain relatively under-explored. To bridge this gap, we introduce MASA-TCN, a novel unified model that leverages the spatial learning capabilities of Temporal Convolutional Networks (TCNs) for EEG emotion regression and classification tasks...
April 23, 2024: IEEE Journal of Biomedical and Health Informatics
#7
JOURNAL ARTICLE
Katerina Deike, Andreas Decker, Paul Scheyhing, Julia Harten, Nadine Zimmermann, Daniel Paech, Oliver Peters, Silka D Freiesleben, Luisa-Sophie Schneider, Lukas Preis, Josef Priller, Eike Spruth, Slawek Altenstein, Andrea Lohse, Klaus Fliessbach, Okka Kimmich, Jens Wiltfang, Claudia Bartels, Niels Hansen, Frank Jessen, Ayda Rostamzadeh, Emrah Düzel, Wenzel Glanz, Enise I Incesoy, Michaela Butryn, Katharina Buerger, Daniel Janowitz, Michael Ewers, Robert Perneczky, Boris-Stephan Rauchmann, Stefan Teipel, Ingo Kilimann, Doreen Goerss, Christoph Laske, Matthias H Munk, Annika Spottke, Nina Roy, Michael Wagner, Sandra Roeske, Michael T Heneka, Frederic Brosseron, Alfredo Ramirez, Laura Dobisch, Steffen Wolfsgruber, Luca Kleineidam, Renat Yakupov, Melina Stark, Matthias C Schmid, Moritz Berger, Stefan Hetzer, Peter Dechent, Klaus Scheffler, Gabor C Petzold, Anja Schneider, Alexander Effland, Alexander Radbruch
OBJECTIVES: Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD...
April 23, 2024: Investigative Radiology
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JOURNAL ARTICLE
Vein segmentation and visualization of upper and lower extremities using convolution neural network.
Amit Laddi, Shivalika Goyal, Himani, Ajay Savlania
OBJECTIVES: The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments...
April 24, 2024: Biomedizinische Technik. Biomedical Engineering
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JOURNAL ARTICLE
Myeongsub Kim, Sukyung Park
In this study, we address two technical challenges to enhance golf swing trajectory accuracy using a wrist-worn inertial sensor: orientation estimation and drift error mitigation. We extrapolated consistent sensor orientation from specific address-phase signal segments and trained the estimation with a convolutional neural network. We then mitigated drift error by applying a constraint on wrist speed at the address, backswing top, and finish, and ensuring that the wrist's finish displacement aligns with a virtual circle on the 3D swing plane...
April 22, 2024: Scientific Reports
#10
JOURNAL ARTICLE
Subhashis Banerjee, Fredrik Nysjö, Dimitrios Toumpanakis, Ashis Kumar Dhara, Johan Wikström, Robin Strand
Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree...
April 22, 2024: Scientific Reports
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JOURNAL ARTICLE
Wen Dang, Jianlong Feng, Delei Li, Mengzhen Fan, Liang Zhao
The relatively short duration of available tide gauge records poses challenges for conducting comprehensive statistical analyses of storm surges in the Western North Pacific. To address this issue, we employ a convolutional neural network model to reconstruct the maximum daily storm surge at 160 tide gauges from 1900 to 2010 in the Western North Pacific. The reconstructed dataset serves multiple purposes. Firstly, it facilitates the identification of regions where notable changes in the storm surges have occurred in the past...
April 22, 2024: Scientific Data
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JOURNAL ARTICLE
Xuying Dong, Wanlin Qiu
This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network...
April 22, 2024: Scientific Reports
#13
JOURNAL ARTICLE
Alex C Ma, Ayana D Cameron, Martin Wiener
Visual stimuli are known to vary in their perceived duration. Some visual stimuli are also known to linger for longer in memory. Yet, whether these two features of visual processing are linked is unknown. Despite early assumptions that time is an extracted or higher-order feature of perception, more recent work over the past two decades has demonstrated that timing may be instantiated within sensory modality circuits. A primary location for many of these studies is the visual system, where duration-sensitive responses have been demonstrated...
April 22, 2024: Nature Human Behaviour
#14
JOURNAL ARTICLE
Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, Huaiqiu Zhu
BACKGROUND: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses. FINDINGS: We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency...
January 2, 2024: GigaScience
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JOURNAL ARTICLE
Xuefang Lu, Wei Gong, Wenbing Yang, Zhoufeng Peng, Chao Zheng, Yunfei Zha
PURPOSE: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR). METHODS: We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022...
April 15, 2024: European Journal of Radiology
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JOURNAL ARTICLE
Muwei Jian, Chen Tao, Ronghua Wu, Haoran Zhang, Xiaoguang Li, Rui Wang, Yanlei Wang, Lizhi Peng, Jian Zhu
BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this study is to improve the segmentation effect of esophageal squamous cell carcinoma lesions...
April 14, 2024: Computer Methods and Programs in Biomedicine
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JOURNAL ARTICLE
Borui Sun, Yiwei Fang, Hui Yang, Fan Meng, Chao He, Yun Zhao, Kai Zhao, Huiping Zhang
To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1...
April 17, 2024: Talanta
#18
JOURNAL ARTICLE
Jichen Xu, Wei Jiang, Jiayi Wu, Wei Zhang, Zhenyu Zhu, Jingmin Xin, Nanning Zheng, Bo Wang
BACKGROUND: Liver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time-consuming, and the labeling results of different clinicians are prone to inconsistencies...
April 22, 2024: Medical Physics
#19
JOURNAL ARTICLE
Abdul Aziz Hulleck, Aamna Alshehhi, Marwan El Rich, Raviha Khan, Rateb Katmah, Mahdi Mohseni, Navid Arjmand, Kinda Khalaf
Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait...
April 22, 2024: IEEE Transactions on Neural Systems and Rehabilitation Engineering
#20
JOURNAL ARTICLE
Xiaoqing Chen, Ziwei Wang, Dongrui Wu
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs...
April 22, 2024: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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