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Deep Learning-Enabled, Computed Tomography-Based Race- and Sex-Specific Epicardial Adipose Tissue Thresholds for Cardiovascular Risk Stratification

Deep Learning-Enabled, Computed Tomography-Based Race- and S...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Buchwald, Mikolaj Shanbhag, Aakash Miller, Robert J.H. Zhang, Wenhao Michalowska, Anna M. Killekar, Aditya Lemley, Mark Kavanagh, Paul B. Fujito, Hidesato Liang, Joanna X. Builoff, Valerie Knight, Stacey Einstein, Andrew J. Miller, Edward J. Feher, Atilla Sinusas, Albert J. Chareonthaitawee, Panithaya Bullock-Palmer, Renee P. Di Carli, Marcelo F. Berman, Daniel S. Dey, Damini Slomka, Piotr J. Departments of Medicine Division of Artificial Intelligence in MedicineImaging and Biomedical Sciences Cedars-Sinai Medical Center Los AngelesCA United States Poznan Supercomputing and Networking Center Polish Academy of Sciences Poznan Poland Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Department of Cardiac Sciences University of Calgary CalgaryAB Canada Center of Radiological Diagnostics National Medical Institute The Ministry of the Interior and Administration Warsaw Poland Department of Cardiology Nihon University Itabashi Hospital Tokyo Japan Intermountain Medical Center Heart Institute MurrayUT United States Division of Cardiology Columbia University Irving Medical Center New York CityNY United States Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New HavenCT United States Department of Cardiovascular Medicine Mayo Clinic RochesterMN United States Department of Cardiology Deborah Heart and Lung Center Browns MillsNJ United States Division of Cardiovascular Medicine Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Cardiovascular Imaging Program Departments of Radiology and Medicine Division of Nuclear Medicine and Molecular Imaging Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA United States

ISBN: (纸本)9781510685987

While the volume of epicardial adipose tissue (EAT) has been linked to various conditions and showed a prognostic value of cardiovascular events, an insufficient effort was put into proposing clear, race- and sex-specific thresholds of high-risk and low-risk EAT amounts. Also, previous works focused on the absolute volume of EAT in milliliters, adjusted to the body size of the patients, and none has proposed a relative measure of what is the amount of EAT for a given patient also in reference to the size of the patient’s heart. In this retrospective study, computed tomography attenuation correction (CTAC) scans from 20,587 patients (58% male;84% White, 13% Black, 3% Asian) from 6 sites were used. With the previously described deep learning (DL)-based method, EAT values per patient were calculated, and subsequently normalized using either the DL-obtained cardiac volume mask, body size, or both. The association between the percent of EAT in reference to the heart size and incident mortality or nonfatal myocardial infarction (MI) was evaluated with Cox models adjusted for age, sex, body mass index, race, cigarette smoking status, hypertension, diabetes, family history of coronary artery disease, dyslipidemia, and left ventricle size. In total, 4,133 events were observed over a period of 6-year follow-up. Our analysis showed that when the amount of EAT was normalized jointly for body surface area and heart size, the predictive effect of EAT was diminished (p=0.85). Patients with higher EAT density, however, were more likely to experience death or MI during follow-up (p © 2025 SPIE.

关键词： Risk assessment

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Machine Learning based Reduced Kernel PCA for Nonlinear Process Monitoring

Machine Learning based Reduced Kernel PCA for Nonlinear Proc...

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International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

作者： Khaled Dhibi Radhia Fezai Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha QATAR

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

Process monitoring is an essential part of industrial systems. It requires higher product quality and safety operations. Therefore, a nonlinear data-driven approach based reduced KPCA (RKPCA) for statistical monitoring of industrial processes is developed. RKPCA is a novel machine learning tool which merges dimensionality reduction and supervised learning. The use of classical KPCA for modeling and monitoring purposes can impose a high computational load when a large number of measurements are recorded. The main idea of the proposed RKPCA approach is to reduce the number of observations (samples) in the data matrix using the Euclidean distance between samples as dissimilarity metric so that only one observation is kept in case of redundancy. The Tennessee Eastman Process (TEP) is used to evaluate the fault detection abilities of the proposed RKPCA technique. The performance of the proposed method is evaluated and compared to the classical KPCA in terms of false alarms rates (FAR), missed detection rates (MDR) and computation times (CT).

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Machine learning based Gaussian process regression for fault detection of Biological Systems

Machine learning based Gaussian process regression for fault...

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International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

作者： Radhia Fezai Majdi Mansouri Nasreddine Bouguila Hazem Nounou Mohamed Nounou Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha QATAR Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

Effective detection of faults in Biological processes is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. Therefore, the first objective of this paper is to develop a machine learning based Gaussian process regression (GPR) technique that can accurately model biological processes and compute the monitored residuals. The second objective is to apply a generalized likelihood ratio test (GLRT) to the evaluated residuals for fault detection purposes. The detection performance of the GPR-based GLRT is evaluated using a biological process representing a Cad System in E. Coli (CSEC) model. The GPR-based GLRT is used to enhance monitoring of the Cad System in E. coli process through monitoring some of the key variables involved in this process, such as enzymes, lysine, and cadaverine.

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Reduced Gaussian process regression for fault detection of chemical processes

Reduced Gaussian process regression for fault detection of c...

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International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

In this paper, a reduced Gaussian process regression (RGPR)-based generalized likelihood ratio test (GLRT) is proposed for fault detection in industrial systems. In contrast to the classical GPR technique, the RGPR model can handle domains with a large number of activities that require very large training sets. The developed RGPR-based GLRT method aims first to build a RGPR model, then, it consists to apply GLRT to the monitored residuals obtained from PGPR for fault detection purposes. The fault detection performance of the developed RGPR-based GLRT method is illustrated through the Tennessee Eastman process. The simulation results show that the RGPR-based GLRT method outperforms the conventional GPR-based GLRT technique in terms of miss detection rate and CPU-time.

关键词： Gaussian process regression (GPR) reduced Gaussian process regression (RGPR) Kmeans fault detection generalized likelihood ratio test (GLRT) chemical processes

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Multi Inteligent Traffic Light Optimization techniques by appling Modified Component Analysis Algorithm

Multi Inteligent Traffic Light Optimization techniques by ap...

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International Conference on Science Technology Engineering and Management (ICONSTEM)

作者： V. Ellappan B. Sindhusaranya Tadesse Hailu Electronics & Communication Eng. Program School of Electrical Engineering & Computing Adama Science and Technology University Adama Ethiopia Center for Research in Image and Signal Processing2 Mahendra Engineering College (Autonomous) Namakkal

ISBN: (纸本)9781728116006

Large cities situated within the developing countries facing one of the most important problems are heavy traffic congestion. It happened because of fast urbanization n. Many modern infrastructures for constructing a traffic signal have been introduced. Due to high maintenance cost and less knowledge of local workers that infrastructure doesn't work well. In this paper, we proposed a new technique for signal control framework, which estimates the traffic flows using the image analysis. For this technique inexpensive Web cameras are required for supporting signal control strategy which is based on traffic volume on that particular time. Traffic volumes can be estimated with few observation points. Based on that, signal controls can be adjusted. Numerical experiments of this proposed work confirmed that average travel time is reduced to 20.6% while comparing to the fixed-time signal control.

关键词： Matlab Graphical user interfaces Traffic control Computational modeling Optimization Timing Real-time systems

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Enhanced Machine Learning Approaches for Diagnosing Building Systems

Enhanced Machine Learning Approaches for Diagnosing Building...

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International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

作者： Sondes Gharsellaoui Majdi Mansouri Shady S. Refaat Haitham Abu-Rub Hassani Messaoud National Higher Engineering School of Tunis Monfleury Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems.

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Gender-Equity Model for Liver Allocation Using Artificial Intelligence (GEMA-AI) for Waiting List Liver Transplant Prioritization

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Clinical Gastroenterology and Hepatology 2025年

作者： Gómez-Orellana, Antonio Manuel Rodríguez-Perálvarez, Manuel Luis Guijo-Rubio, David Gutiérrez, Pedro Antonio Majumdar, Avik McCaughan, Geoffrey W. Taylor, Rhiannon Tsochatzis, Emmanuel A. Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Instituto Maimónides de Investigación Biomédica de Córdoba Córdoba Spain Department of Hepatology and Liver Transplantation Hospital Universitario Reina Sofía Instituto Maimónides de Investigación Biomédica de Córdoba Córdoba Spain Department of Medicine University of Córdoba Córdoba Spain Centro de investigación biomédica en red de enfermedades hepáticas y digestivas Madrid Spain Department of Signal Processing and Communications University of Alcalá Alcalá de Henares Spain AW Morrow Gastroenterology and Liver Centre and Australian National Liver Transplant Unit Royal Prince Alfred Hospital Sydney NSW Australia Central Clinical School University of Sydney Sydney NSW Australia Victorian Liver Transplant Unit Austin Health Melbourne VIC Australia Department of Medicine University of Melbourne Melbourne Australia Liver Injury and Cancer Program Centenary Institute Sydney Australia Department of Statistics and Clinical Studies NHS Blood and Transplant Stoke Gifford United Kingdom Sheila Sherlock Liver Unit and UCL Institute for Liver and Digestive Health Royal Free Hospital London United Kingdom

Background & Aims: We aimed to develop and validate an artificial intelligence score (gender-equity model for liver allocation using artificial intelligence [GEMA-AI]) to predict liver transplantation (LT) waiting list outcomes using the same input variables contained in existing models. Methods: This was a cohort study including adult LT candidates enlisted in the United Kingdom (2010–2020) for model training and internal validation and in Australia (1998–2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Hospital glomerular filtration rate in an explainable artificial neural network. GEMA-AI was compared with gender-equity model for liver allocation corrected by serum sodium (GEMA-Na), Model for End-Stage Liver Disease 3.0, and Model for End-Stage Liver Disease corrected by serum sodium for waiting list prioritization. Results: The study included 9320 patients: 5762 in the training cohort, 1920 in the internal validation cohort, and 1638 in the external validation cohort. The prevalence of 90-day mortality or delisting for sickness ranged from 5.3% to 6% across different cohorts. GEMA-AI showed better discrimination than GEMA-Na, Model for End-Stage Liver Disease corrected by serum sodium, and Model for End-Stage Liver Disease 3.0 in the internal and external validation cohorts, with a more pronounced benefit in women and in patients showing at least 1 extreme analytical value. Accounting for identical input variables, the transition from a linear to a nonlinear score (from GEMA-Na to GEMA-AI) resulted in a differential prioritization of 6.4% of patients within the first 90 days and would potentially save 1 in 59 deaths overall, and 1 in 13 deaths among women. Results did not substantially change when ascites was not included in the models. Conclusions: The use of explainable machine learning models may be preferred over conventional regression-based models for waiting list prioritization in LT. GE

关键词： Artificial Neural Networks Disparities Explainable Artificial Intelligence Gender Liver Allocation Machine Learning

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Mitigating Intentional Electromagnetic Interferences over the GSM-R System with Adaptive Filters

Mitigating Intentional Electromagnetic Interferences over th...

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International Conference on Applied Electromagnetics and Communications (ICECom)

作者： Artur N. de São José Virginie Deniau Úrsula do C. Resende Ricardo Adriano Graduate Program in Electrical Engineering Federal University of Minas Gerais French Institute of Science and Technology for Transport France Laboratory on Electronics Waves and Signal Processing for Transport French Institute of Science and Technology for Transport Development and Networks Villeneuve d’Ascq France Eletrical Engineering Department Federal Center for Technological Education of Minas Gerais Belo Horizonte Brazil Eletrical Engineering Department Federal University of Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9781728155142

ISBN: (纸本)9781728155159

Intentional electromagnetic interference is a potential issue for railway networks based on ground-to-train communications. Reported mitigation techniques usually rely on electromagnetic compatibility classical procedures, which are not always the best option for such an unpredictable event. In this work, offline adaptive filtering is used to recover global system for mobile communications - railway damaged data.

关键词： Jamming Rail transportation Base stations Time-frequency analysis Downlink Noise measurement Convergence

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Correction: Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

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BMC genomics 2023年第1期24卷 388页

作者： Elena V Demidova Ilya G Serebriiskii Ramilia Vlasenkova Simon Kelow Mark D Andrake Tiffiney R Hartman Tatiana Kent James Virtucio Gail L Rosen Richard T Pomerantz Roland L Dunbrack Erica A Golemis Michael J Hall David Y T Chen Mary B Daly Sanjeevani Arora Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Kazan Federal University Kazan 420008 Russia. Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia PA 19111 USA. Department of Biochemistry and Molecular Biophysics University of Pennsylvania Philadelphia PA 19104 USA. Arcadia University Glenside PA USA. Department of Biochemistry & Molecular Biology Sidney Kimmel Cancer Center Thomas Jeferson University Philadelphia PA 19107 USA. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University Philadelphia PA 19104 USA. Department of Cancer and Cellular Biology Lewis Katz School of Medicine Philadelphia PA 19140 USA. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Department of Surgical Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu.

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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