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Digital Health during COVID-19: Informatics Dialogue with the World Health Organization

Yearbook of Medical Informatics 2021年第1期30卷 13-16页

作者： Koch, Sabine Hersh, William R. Bellazzi, Riccardo Leong, Tze Yun Yedaly, Moctar Al-Shorbaji, Najeeb Health Informatics Centre Department of Learning Informatics Management and Ethics Karolinska Institutet Stockholm Sweden Department of Medical Informatics & Clinical Epidemiology School of Medicine Oregon Health & Science University Portland OR United States Department of Electrical Computer and Biomedical Engineering University of Pavia Italy IRCCS ICS Maugeri Pavia Italy Department of Computer Science School of Computing National University of Singapore and AI Singapore Singapore Information Society Division African Union Commission Addis Ababa Ethiopia eHealth Development Association Amman Jordan

Background: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic. Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs). Methods: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants’ consent and summarized in this paper. Results: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general;(b) ethics and artificial intelligence;(c) digital divide;(d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic. Conclusions: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly. © 2021 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Artificial Intelligence COVID-19 Global Health Medical Informatics World Health Organization

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Assessing the reliability and validity ranges of magnetic characterization methods

arXiv

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arXiv 2020年

作者： Kouhpanji, Mohammad Reza Zamani Stadler, Bethanie J.H. Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Chemical Engineering and Materials Science University of Minnesota Twin Cities United States

Evaluating the interaction fields of magnetic nanowires (MNWs) is of utmost importance for advancing their functionality in diverse applications including spintronic devices and nanomedicine. In recent years, several quantitative methods have been proposed and become inevitable tools to quantify the interaction fields and decouple their effects from the coercivity. However, the uncertainty of the attained results arose countless open questions leading to discrepancies among the literature. Here, we employ our novel experimental method, named the projection method, to resolve these discrepancies. Using a comparative analysis of the four most commonly used methods (hysteresis loops method, remanence curves method, first-order reversal curve method, and projection method), we unambiguously explicate the reliability and validity limits of these methods to elucidate the origin of the discrepancies. We show that the remanence curves method must solely be used for quantifying the interaction fields if they are considerably weaker compared to the coercivity. Furthermore, we show that both remanence curves method and first-order reversal curve method fail to fully decouple the interaction fields’ effects from the coercivity, similar to the hysteresis loops method. Copyright © 2020, The Authors. All rights reserved.

关键词： Coercive force

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3D-Printed electrochemical sensor-integrated transwell systems

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Microsystems & Nanoengineering 2020年第1期6卷 357-368页

作者： Pradeep Ramiah Rajasekaran Ashley Augustiny Chapin David N.Quan Jens Herberholz William E.Bentley Reza Ghodssi Institute for Systems Research University of MarylandCollege ParkMDUSA Fischell Department of Bioengineering University of MarylandCollege ParkMDUSA Department of Psychology and Neuroscience and Cognitive Science Program University of MarylandCollege ParkMDUSA Institute for Bioscience and Biotechnology Research University of MarylandCollege ParkMDUSA Robert E.Fischell Institute for Biomedical Devices University of MarylandCollege ParkMDUSA Department of Electrical and Computer Engineering University of MarylandCollege ParkMDUSA

This work presents a 3D-printed,modular,electrochemical sensor-integrated transwell system for monitoring cellular and molecular events in situ without sample extraction or microfluidics-assisted downstream *** additive manufacturing techniques such as 3D printing,shadow masking,and molding are used to fabricate this modular system,which is autoclavable,biocompatible,and designed to operate following standard operating protocols(SOPs)of cellular *** to the platform is a flexible porous membrane,which is used as a cell culture substrate similarly to a commercial transwell *** electrochemical sensors fabricated on the membrane allow direct access to cells and their products.A pair of gold electrodes on the top side of the membrane measures impedance over the course of cell attachment and growth,characterized by an exponential decrease(~160%at 10Hz)due to an increase in the double layer capacitance from secreted extracellular matrix(ECM)*** voltammetry(CV)sensor electrodes,fabricated on the bottom side of the membrane,enable sensing of molecular release at the site of cell culture without the need for downstream ***-time detection of ferrocene dimethanol injection across the membrane showed a three order-of-magnitude higher signal at the membrane than in the bulk media after reaching *** modular sensor-integrated transwell system allows unprecedented direct,real-time,and noninvasive access to physical and biochemical information,which cannot be obtained in a conventional transwell system.

关键词： transwell electrochemical culture

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Supertransport of excitons in atomically thin organic semiconductors at the 2D quantum limit

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Light(Science & Applications) 2020年第1期9卷 907-919页

作者： Ankur Sharma Linglong Zhang Jonathan O.Tollerud Miheng Dong Yi Zhu Robert Halbich Tobias Vogl Kun Liang Hieu T.Nguyen Fan Wang Shilpa Sanwlani Stuart K.Earl Daniel Macdonald Ping Koy Lam Jeffrey A.Davis Yuerui Lu Research School of Electrical Energy and Materials EngineeringCollege of Engineering and Computer ScienceThe Australian National UniversityCanberraACT 2601Australia Optical Sciences Centre Swinburne University of TechnologyHawthornVIC 3122Australia ARC Centre of Excellence for Future Low-Energy Electronics Technology Australia Centre for Quantum Computation and Communication Technology Department of Quantum ScienceResearch School of Physics and EngineeringThe Australian National UniversityActonACT 2601Australia School of Mechatronical Engineering Beijing Institute of TechnologyBeijing 100081China Institute for Biomedical Materials and Devices(IBMD) Faculty of ScienceUniversity of Technology SydneySydneyNSW 2007Australia

Long-range and fast transport of coherent excitons is important for the development of high-speed excitonic circuits and quantum computing ***,most of these coherent excitons have only been observed in some low-dimensional semiconductors when coupled with cavities,as there are large inhomogeneous broadening and dephasing effects on the transport of excitons in their native states in ***,by confining coherent excitons at the 2D quantum limit,we first observed molecular aggregation-enabled‘supertransport’of excitons in atomically thin two-dimensional(2D)organic semiconductors between coherent states,with a measured high effective exciton diffusion coefficient of ~346.9 cm^(2)/s at room *** value is one to several orders of magnitude higher than the values reported for other organic molecular aggregates and low-dimensional inorganic *** coupling to any optical cavities,the monolayer pentacene sample,a very clean 2D quantum system(~1.2 nm thick)with high crystallinity(J-type aggregation)and minimal interfacial states,showed superradiant emission from Frenkel excitons,which was experimentally confirmed by the temperature-dependent photoluminescence(PL)emission,highly enhanced radiative decay rate,significantly narrowed PL peak width and strongly directional in-plane *** coherence in monolayer pentacene samples was observed to be delocalised over~135 molecules,which is significantly larger than the values(a few molecules)observed for other organic thin *** addition,the supertransport of excitons in monolayer pentacene samples showed highly anisotropic *** results pave the way for the development of future high-speed excitonic circuits,fast OLEDs,and other optoelectronic devices.

关键词： transport quantum coherent

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GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

arXiv

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arXiv 2023年

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Department of Radiology Netherlands Cancer Institute Amsterdam Netherlands National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China Shenzhen RayShape Medical Technology Co. Ltd China Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness. © 2023, CC BY.

关键词： Medical applications

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Kernel regression imputation in manifolds via bi-linear modeling: The dynamic-MRI case

TechRxiv

引用

TechRxiv 2021年

作者： Slavakis, Konstantinos Shetty, Gaurav N. Cannelli, Loris Scutari, Gesualdo Nakarmi, Ukash Ying, Leslie The Department of Information and Communications Engineering of Tokyo Institute of Technology Tokyo Japan PA United States The Dalle Molle Institute for Artificial Intelligence IDSIA/SUPSI Lugano Switzerland The Department of Industrial Engineering of Purdue University West LafayetteIN United States The Department of Computer Science and Computer Engineering University of Arkansas FayettevilleAR United States The Departments of Electrical Engineering and Biomedical Engineering of University at Buffalo SUNY BuffaloNY United States

This paper introduces a non-parametric approximation framework for imputation-by-regression on data with missing entries. The proposed framework, coined kernel regression imputation in manifolds (KRIM), is built on the hypothesis that features, generated by the measured data, lie close to an unknown-to-the-user smooth manifold. The feature space, where the smooth manifold is embedded in, takes the form of a reproducing kernel Hilbert space (RKHS). Aiming at concise data descriptions, KRIM identifies a small number of "landmark points" to define approximating "linear patches" in the feature space which mimic tangent spaces to smooth manifolds. This geometric information is infused into the design through a novel bi-linear model that allows for multiple approximating RKHSs. To effect imputation-by-regression, a bi-linear inverse problem is solved by an iterative algorithm with guaranteed convergence to a stationary point of a non-convex loss function. To showcase KRIM’s modularity, the application of KRIM to dynamic magnetic resonance imaging (dMRI) is detailed, where reconstruction of images from severely under-sampled dMRI data is desired. Extensive numerical tests on synthetic and real dMRI data demonstrate the superior performance of KRIM over state-of-the-art approaches under several metrics and with a small computational footprint. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse biomedical Data Sources

arXiv

引用

arXiv 2023年

作者： Rajendran, Suraj Pan, Weishen Sabuncu, Mert R. Chen, Yong Zhou, Jiayu Wang, Fei Tri-Institutional Computational Biology & Medicine Program Cornell University NY United States Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States School of Electrical and Computer Engineering Cornell University IthacaNY United States Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medical School New YorkNY United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Department of Computer Science and Engineering Michigan State University East LansingMI United States

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce "patchwork learning" (PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows. © 2023, CC BY.

关键词： Data privacy

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A spanning tree-based genetic algorithm for distribution network reconfiguration

arXiv

引用

arXiv 2020年

作者： Gautam, Mukesh Bhusal, Narayan Benidris, Mohammed Louis, Sushil J. Department of Electrical and Biomedical Engineering Department of Computer Science and Engineering University of Nevada RenoNV89557 United States

This paper presents a spanning tree-based genetic algorithm (GA) for the reconfiguration of electrical distribution systems with the objective of minimizing active power losses. Due to low voltage levels at distribution systems, power losses are high and sensitive to system configuration. Therefore, optimal reconfiguration is an important factor in the operation of distribution systems to minimize active power losses. Smart and automated electric distribution systems are able to reconfigure as a response to changes in load levels to minimize active power losses. The proposed method searches spanning trees of potential configurations and finds the optimal spanning tree using genetic algorithm in two steps. In the first step, all invalid combinations of branches and tie-lines (i.e., switching combinations that do not provide power to some of loads or violate the radiality and connectivity conditions) generated by initial population of GA are filtered out with the help of spanning tree search algorithm. In the second step, power flow analyses are performed only for combinations that form spanning trees. The optimal configuration is then determined based on the amount of active power losses (optimal configuration is the one that results in minimum power losses). The proposed method is implemented on several systems including the well-known 33-node and 69-node systems. The results show that the proposed method is accurate and efficient in comparison with existing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Genetic algorithms

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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes

arXiv

引用

arXiv 2025年

作者： Rego, Brenner S. Raffo, Guilherme V. Terra, Marco H. Scott, Joseph K. Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Electronics Engineering the Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Numerical methods

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Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI

Adaptive Aggregation Weights for Federated Segmentation of P...

引用

IEEE International Symposium on biomedical Imaging

作者： Hongyi Pan Gorkem Durak Zheyuan Zhang Yavuz Taktak Elif Keles Halil Ertugrul Aktas Alpay Medetalibeyoglu Yury Velichko Concetto Spampinato Ivo Schoots Marco J. Bruno Rajesh N. Keswani Pallavi Tiwari Candice Bolan Tamas Gonda Michael G. GORRins Michael B. Wallace Ziyue Xu UIas Bagci Department of Radiology Northwestern University Chicago IL USA Department of Radiology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Internal Medicine Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Radiology and Nuclear Medicine Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Henatology Northwestern University Chicago IL USA Department of Biomedical Engineering and-Radiology University of Wisconsin Madison WI USA Division of Gastroenterology and Hepatology Mayo Clinic Jacksonville FL USA Division of Gastroenterology and Hepatology New York University New York NY USA Division of Gastroenterology and Henatology Johns Hopkins University Baltimore MD USA NVIDIA Bethesda MD USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in general-izing across domains due to variations in imaging protocols and pa-tient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that in-corporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers).

关键词： Training Image segmentation Adaptation models Protocols Accuracy Federated learning Magnetic resonance imaging Data models Pancreas biomedical imaging

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