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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia 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

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

A Comparative Study of Z- Transform and Fourier Transform applied on Medical Images for Detection of Cancer Segments 3

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A Comparative Study of Z- Transform and Fourier Transform ap...

3rd International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2019

作者： Santra, Soumen Mandal, Surajit Das, Kunal Bhattacharjee, Joy Deyasi, Arpan Dept. of Computer Application Techno India International Kolkata India B.P.Poddar Inst. of Mgmt. and Technology Dept. of Electronics and Communication Engineering Kolkata India Narula Institute of Technology Dept. of Computer Science and Communication Engineering Kolkata India A.P.C College Dept. of Electronic Science Barasat India RCC Institute of Information Technology Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9781728155432

A preliminary investigation is carried out by applying Z-transform technique on different types of images with sole intention to record the z-score of any binary image. Bandwidth of the images are computed, and compared with that obtained from Fourier technique. Length of z-score matrix is graphically represented. Result is also analyzed for smooth, shiny and rough images. This preliminary investigation will help in sending multimedia content, precisely images through network and in signal processing which helps for the segments of cancerous portion. © 2019 IEEE.

关键词： Medical imaging

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Enhanced design matrix for task-related fMRI data analysis

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NeuroImage 2021年 245卷 118719-118719页

作者： Morante, Manuel Kopsinis, Yannis Chatzichristos, Christos Protopapas, Athanassios Theodoridis, Sergios Dept. of Electronic Systems Aalborg University Denmark Computer Technology Institutes & Press “Diophantus” (CTI) Patras Greece LIBRA MLI Ltd Edinburgh United Kingdom Dept. Electrical Engineering (ESAT) Dynamical Systems Signal Processing and Data Analytics (STADIUS) KU Leuven Belgium Dept. of Special Needs Education of the University of Oslo Norway Dept. of Informatics and Telecommunications of the National and Kapodistrian University of Athens Greece

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology. © 2021

关键词： Dictionary learning fMRI General linear model (GLM) Semi-blind Subject variability

A Location History-Aware Retail Product Recommender System

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A Location History-Aware Retail Product Recommender System

IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Thomas Chatzidimitris Damianos Gavalas Vlasios Kasapakis Charalampos Konstantopoulos Damianos Kypriadis Grammati Pantziou Christos Zaroliagis Dept of Cultural Technology & Communication University of the Aegean Mytilene Greece Dept of Product & Systems Design Engineering University of the Aegean Syros Greece Dept of Informatics University of Piraeus Piraeus Greece Dept of Informatics & Computer Engineering University of West Attica Athens Greece Dept of Computer Engineering & Informatics University of Patras Patras Greece

ISBN: (纸本)9781728133171

Mobile Context-Aware Recommender Systems (CARS) have recently emerged aiming at generating recommendations relevant to the specific environmental and situational usage context. This article reports on the design and implementation of a collaborative filtering-based mobile CARS, developed as part of an e-platform that supports location-based search for retail products and services sold by nearby physical retailer shops. In addition to the current user location, our RS considers a multitude of contextual factors like time, season, demographic data, consumer behavior, and location history of the user in order to derive more meaningful product recommendations. The RS has been tested on real operational environments as well as on lab evaluation trials demonstrating higher accuracy and relevance of recommendations against two baseline approaches.

关键词： History Filtering Prediction algorithms Mobile computing Automobiles Clustering algorithms Wireless communication

A new malware detection system using a high performance-ELM method

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arXiv 2019年

作者： Shamshirband, Shahab Chronopoulos, Anthony T. Department of Computer and Information Science Norwegian University of Science and Technology Norway Department of Computer Science University of Texas at San Antonio United States Dept of Computer Engineering & Informatics University of Patras Greece

A vital element of a cyberspace infrastructure is cybersecurity. Many protocols proposed for security issues, which leads to anomalies that affect the related infrastructure of cyberspace. Machine learning (ML) methods used to mitigate anomalies behavior in mobile devices. This paper aims to apply a High Performance Extreme Learning Machine (HP-ELM) to detect possible anomalies in two malware datasets. Two widely used datasets (the CTU-13 and Malware) are used to test the effectiveness of HP-ELM. Extensive comparisons are carried out in order to validate the effectiveness of the HP-ELM learning method. The experiment results demonstrate that the HP-ELM was the highest accuracy of performance of 0.9592 for the top 3 features with one activation function. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

Using LoRa technology for IoT Monitoring Systems

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Using LoRa Technology for IoT Monitoring Systems

International Conference on the Network of the Future (NOF)

作者： Christos Bouras Apostolos Gkamas Vasileios Kokkinos Nikolaos Papachristos Computer Technology Institute & Press "Diophantus" Patras Greece University Ecclesiastical Academy of Vella Greece Computer Engineering and Informatics Dept. Univ. of Patras Greece

ISBN: (数字)9781728144450

ISBN: (纸本)9781728144467

This paper presents technology comparison scenarios for Internet of Things (IoT) concepts on rescue monitoring. The study starts by comparing WiFi & LoRa as wireless technologies able to be used by smart devices for data transmission. The IoT end-devices used in these concepts have high requirements in battery saving and for this reason the usage of low-power modules is advisable. This paper focus in rescue monitoring and the goal in the current study is the usage of the two wireless technologies used for data transmission from IoT devices: the already known WiFi and the upcoming LoRa technology. During rescue monitoring, important concepts are the identification and individuals' rescue of particularly vulnerable groups or individuals belonging to population groups with a high probability of being lost. A LoRa based gateway and WiFi Router is used to connect the end-devices used in our scenarios to the Internet. The collected data on server application as captured from installed sensors on the IoT modules can be displayed to authorized users through a web or mobile application. The results through simulation and real time experiments indicate that LoRa could be an ideal candidate for rescue monitoring. This study is a first step in creating a more general ecosystem for rescue concepts including all the hardware and software using the LoRa technology as transmission method.

关键词： Wireless fidelity Monitoring Sensors Batteries Logic gates Servers Internet of Things

Goal-Aware RSS for Complex Scenarios via Program Logic

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arXiv 2022年

作者： Hasuo, Ichiro Eberhart, Clovis Haydon, James Dubut, Jérémy Bohrer, Rose Kobayashi, Tsutomu Pruekprasert, Sasinee Zhang, Xiao-Yi Pallas, Erik André Yamada, Akihisa Suenaga, Kohei Ishikawa, Fuyuki Kamijo, Kenji Shinya, Yoshiyuki Suetomi, Takamasa Tokyo101-8430 Japan Dept. Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609-2280 United States Inst. Software & Systems Engineering University of Augsburg Universitätstraße 6a AugsburgD-86135 Germany Cyber Physical Security Research Center AIST Aomi 2-4-7 Tokyo135-0064 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Mazda Motor Corporation Fuchu730-8670 Japan SOKENDAI The Graduate University for Advanced Studies Japan Tokyo Japan

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules;we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement. © 2022, CC BY-NC-ND.

关键词： computer circuits

Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach

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arXiv 2024年

作者： Piao, Chengzhe Zhu, Taiyu Wang, Yu Baldeweg, Stephanie E. Taylor, Paul Georgiou, Pantelis Sun, Jiahao Wang, Jun Li, Kezhi Institute of Health Informatics University College London LondonNW1 2DA United Kingdom Department of Psychiatry University of Oxford OxfordOX3 7JX United Kingdom Department of Diabetes & Endocrinology University College London Hospitals LondonNW1 2PG United Kingdom Centre for Obesity & Metabolism Dept of Experimental & Translational Medicine University College London LondonWC1E 6JF United Kingdom Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom Department of Computer Science University College London LondonWC1E 6EA United Kingdom

Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant "cold start" problem in patient care. Utilizing population models to address this challenge is a potential solution, but collecting patient data for training population models in a privacy-conscious manner is challenging, especially given that such data is often stored on personal devices. Considering the privacy protection and addressing the "cold start" problem in diabetes care, we propose "GluADFL", blood Glucose prediction by Asynchronous Decentralized Federated Learning. We compared GluADFL with eight baseline methods using four distinct T1D datasets, comprising 298 participants, which demonstrated its superior performance in accurately predicting BG levels for cross-patient analysis. Furthermore, patients’ data might be stored and shared across various communication networks in GluADFL, ranging from highly interconnected (e.g., random, performs the best among others) to more structured topologies (e.g., cluster and ring), suitable for various social networks. The asynchronous training framework supports flexible participation. By adjusting the ratios of inactive participants, we found it remains stable if less than 70% are inactive. Our results confirm that GluADFL offers a practical, privacy-preserving solution for BG prediction in T1D, significantly enhancing the quality of diabetes management. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting