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Cross-sectional and longitudinal Biomarker extraction and analysis for multicentre FLAIR brain MRI

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Neuroimage: Reports 2022年第2期2卷 100091-100091页

作者： DiGregorio, J. Gibicar, A. Khosravani, H. Maralani, P. Jabehdar Tardif, J.-C. Tyrrell, P.N. Moody, A.R. Khademi, A. Electrical Computer and Biomedical Engineering Dept. Ryerson University Toronto ON Canada Division of Neurology Department of Medicine University of Toronto Toronto ON Canada Department of Medical Imaging University of Toronto Toronto ON Canada Montreal Heart Institute Montreal QU Canada Department of Medicine Université de Montréal QU Canada Department of Statistical Science University of Toronto Toronto ON Canada Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada Institute for Biomedical Engineering Science and Technology (iBEST) A Partnership Between St. Michael's Hospital and Ryerson University Canada

Fluid-attenuated inversion recovery (FLAIR) MRI has emerged as an important sequence for the analysis of cerebrovascular (CVD) and Alzheimer's disease (AD). Large-scale, automated cross-sectional and longitudinal cerebral biomarker extraction from FLAIR datasets could progress disease characterization, improve disease monitoring, and help to determine optimal intervention times. Despite this, most automated biomarker extraction algorithms are designed for T1-weighted or multi-modal inputs. In this work, automated tools were used to extract biomarkers from large, FLAIR-only datasets to evaluate the feasibility of this sequence to characterize healthy, AD, and CVD subjects in a similar manner to traditional approaches. Total brain volume (TBV), cerebrospinal fluid (CSF) volume, and white matter lesion (WML) volume was measured for the cross-sectional biomarkers and the corresponding annual rates of change over multiple scans represented the longitudinal biomarkers. Biomarkers were extracted from two dementia cohorts (4356 vol, 162 233 images) and one vascular disease cohort (869 vol, 42 850 images) using deep learning-based segmentation algorithms designed specifically for FLAIR. Biomarkers from all cohorts were summarized using descriptive statistics, correlation analysis, and ANCOVA to assess differences in diagnostic labels while accounting for demographic and acquisition factors. Biomarkers from FLAIR MRI had similar trends with those extracted from traditional modalities in the literature for characterizing healthy, AD, and CVD subjects. This demonstrates that FLAIR MRI can be used for end-to-end analysis of large AD and CVD datasets, which can lower acquisition costs, simplify clinical translation, and reduce measurement error associated with multi-modal approaches. © 2022 The Author(s)

关键词： Alzheimer's disease Cerebral biomarkers Cerebrovascular disease FLAIR MRI

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

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arXiv 2021年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Nodehi, Issa Mashmool, Amir Zadegan, Zeynab Kiani Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Tamadon, Tahereh Khosravi, Samiyeh Kohnehshari, Mitra Akbari Hassannatajjeloudari, Edris Sharifrazi, Danial Mosavi, Amir Loh, Hui Wen Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Engineering Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering University of Qom Qom Iran Italy Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Engineering Faculty Bu-Ali Sina University Hamedan Iran Department of Nursing School of Nursing and Allied Medical Sciences Maragheh Faculty of Medical Sciences Maragheh Iran Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Faculty of Informatics Obuda University Budapest Hungary School of Science and Technology Singapore University of Social Sciences Singapore Department of Cardiology National Heart Centre Singapore Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals. © 2021, CC BY-SA.

关键词： Deep neural networks

A cost-efficient protocol for open blockchains 5

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A cost-efficient protocol for open blockchains

5th International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2019

作者： Li, Chunlei Rong, Chunming Jaatun, Martin Gilje Department of Informatics University of Bergen Bergen Norway Dept. of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

Current proof-of-work blockchains are not sustainable in terms of energy needed to run them. In this paper we propose a new scheme that avoids wasted proof-of-work by a dynamic probabilistic method, where the consensu... 详细信息

ISBN: (纸本)9781728102290

关键词： Blockchain

CADA: Multi-scale collaborative adversarial domain adaptation for unsupervised optic disc and cup segmentation

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arXiv 2021年

作者： Liu, Peng Tran, Charlie T. Kong, Bin Fang, Ruogu J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida GainesvilleFL32611 United States Dept. of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Center for Cognitive Aging and Memory University of Florida GainesvilleFL32611 United States Department of Computer Science Unc Charlotte CharlotteNC28223 United States

Recently, deep neural networks have demonstrated comparable and even better performance than board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one domain to new testing domains. In this paper, we propose a multi-scale input along with multiple domain adaptors applied hierarchically in both feature and output spaces. The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge. Multi-scale inputs can reduce the information loss due to the pooling layers used in the network for feature extraction, while our proposed CADA is an interactive paradigm that presents an exquisite collaborative adaptation through both adversarial learning and ensembling weights at different network layers. In particular, to produce a better prediction for the unlabeled target domain data, we simultaneously achieve domain invariance and model generalizability via adversarial learning at multi-scale outputs from different levels of network layers and maintaining an exponential moving average (EMA) of the historical weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier. Meanwhile, the ensembling of weights via EMA reduces the uncertainty of adapting multiple discriminator learning. Comprehensive experimental results demonstrate that our CADA model incorporating multi-scale input training can overcome performance degradation and outperform state-of-the-art domain adaptation methods in segmenting retinal optic disc and cup from fundus images stemming from the REFUGE, Drishti-GS, and Rim-One-r3 datasets. The code will be avai

关键词： Network layers

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

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arXiv 2021年

作者： Sadeghi, Delaram Shoeibi, Afshin Ghassemi, Navid Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Teshnehlab, Mohammad Gorriz, Juan M. Khozeimeh, Fahime Zhang, Yu-Dong Nahavandi, Saeid Acharya, U Rajendra Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Biomedical Engineering Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom Department of Informatics University of Leicester Leicester United Kingdom Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, flowing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end. © 2021, CC BY-NC-ND.

关键词： Diseases

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms

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arXiv 2022年

作者： Ding, Cheng Guo, ZhiCheng Rudin, Cynthia Xiao, Ran Shah, Amit Do, Duc H. Lee, Randall J. Clifford, Gari Nahab, Fadi B. Hu, Xiao Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States School of Nursing Emory University AtlantaGA United States Dept of Epidemiology RSPH Emory University School of Medicine AtlantaGA United States Department of Medicine Emory University School of Medicine AtlantaGA United States School of Medicine University of California at Los Angeles Los AngelesCA United States School of Medicine University of California at San Francisco San FranciscoCA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Neurology Emory University School of Medicine AtlantaGA United States

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, further advancement of this paradigm of continuous AF detection in ambulatory settings, towards a population-wide screening use case, still faces several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in multiple aspects including handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency. © 2022, CC BY.

关键词： Large dataset

Microwave Frequency Fiber Interferometry in Submarine Deployed Telecommunication Cables

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arXiv 2025年

作者： Bogris, A. Simos, C. Simos, I. Wang, Y. Fichtner, A. Deligiannidis, S. Melis, N.S. Mesaritakis, C. Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo12243 Greece Dept. of Physics University of Thessaly Lamia35100 Greece Department of Electrical and Electronics Engineering University of West Attica Egaleo12244 Greece Department of Earth and Planetary Sciences ETH Zurich Zurich Switzerland National Observatory of Athens Institute of Geodynamics Athens Greece Department of Biomedical Engineering University of West Attica Aghiou Spiridonos Egaleo12243 Greece

We operated a microwave frequency fiber interferometer in a telecommunication cable in the Ionian Sea, Greece, for two months. The capability of detecting undersea micro earthquakes (magnitude~1.5), tides and ocean wa... 详细信息

关键词： Interferometry

Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

Blockchain-based recommender systems: Applications, challenges and future opportunities

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arXiv 2021年

作者： Himeur, Yassine Sayed, Aya Alsalemi, Abdullah Bensaali, Faycal Amira, Abbes Varlamis, Iraklis Eirinaki, Magdalini Sardianos, Christos Dimitrakopoulos, George Department of Electrical Engineering Qatar University Doha Qatar Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Computer Science University of Sharjah UAE Dept. of Informatics and Telematics Harokopio University of Athens Greece San Jose State University San JoseCA United States

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models’ accuracy and ignore issues related to security and the users’ privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users’ private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. © 2021, CC BY.

关键词： Blockchain