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arXiv 2020年

作者： Zhou, S. Kevin Greenspan, Hayit Davatzikos, Christos Duncan, James S. van Ginneken, Bram Madabhushi, Anant Prince, Jerry L. Rueckert, Daniel Summers, Ronald M. School of Biomedical Engineering University of Science and Technology of China Institute of Computing Technology Chinese Academy of Sciences China Biomedical Engineering Department Tel-Aviv University Israel Radiology Department Electrical and Systems Engineering Department University of Pennsylvania United States The Departments of Biomedical Engineering and Radiology & Biomedical Imaging Yale University United States Radboud University Medical Center Netherlands The Department of Biomedical Engineering Case Western Reserve University Louis Stokes Cleveland Veterans Administration Medical Center United States Electrical and Computer Engineering Department Johns Hopkins University United States Klinikum rechts der Isar TU Munich Germany Department of Computing Imperial College United Kingdom National Institute of Health Clinical Center United States

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Glassy carbon microelectrode arrays enable voltage-peak separated simultaneous detection of dopamine and serotonin using fast scan cyclic voltammetry

arXiv

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arXiv 2020年

作者： Castagnola, Elisa Thongpang, Sanitta Hirabayashi, Mieko Nava, Giorgio Nimbalkar, Surabhi Nguyen, Tri Lara, Sandra Oyawalea, Alexis Bunnella, James Moritz, Chet Kassegne, Sam NanoFAB. SDSU Lab Department of Mechanical Engineering San Diego State University San DiegoCA United States University of Washington Departments of Electrical & Computer Engineering Rehabilitation Medicine and Physiology & Biophysics SeattleWA United States Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand Department of Mechanical Engineering University of California Riverside RiversideCA United States Bill & Melinda Gates Center for Computer Science & Engineering SeattleWA98195 United States

Progress in real-time, simultaneous in vivo detection of multiple neurotransmitters will help accelerate advances in neuroscience research. The need for development of probes capable of stable electrochemical detection of rapid neurotransmitter fluctuations with high sensitivity and selectivity and sub-second temporal resolution has, therefore, become compelling. Additionally, a higher spatial resolution multi-channel capability is required to capture the complex neurotransmission dynamics across different brain regions. These research needs have inspired the introduction of glassy carbon (GC) microelectrode arrays on flexible polymer substrates through carbon MEMS (C-MEMS) microfabrication process followed by a novel pattern transfer technique. These implantable GC microelectrodes offer unique advantages in electrochemical detection of electroactive neurotransmitters through the presence of active carboxyl, carbonyl, and hydroxyl functional groups. In addition, they offer fast electron transfer kinetics, capacitive electrochemical behavior, and wide electrochemical window. Here, we combine the use of these GC microelectrodes with the fast scan cyclic voltammetry (FSCV) technique to optimize the co-detection of dopamine and serotonin in vitro and in vivo. We demonstrate that using optimized FSCV triangular waveform at scan rates ≤ 700 V/s and holding and switching at potentials of 0.4 and 1V respectively, it is possible to discriminate voltage reduction and oxidation peaks of serotonin and dopamine, with serotonin contributing distinct multiple oxidation peaks. Taken together, our results present a compelling case for a carbon-based MEA platform rich with active functional groups that allows for repeatable and stable detection of electroactive multiple neurotransmitters at concentrations as low as 10 nM. © 2020, CC-BY.

关键词： Carbon

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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A comparative study of eight human auditory models of monaural processing

arXiv

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arXiv 2021年

作者： Vecchi, Alejandro Osses Varnet, Léo Carney, Laurel H. Dau, Torsten Bruce, Ian C. Verhulst, Sarah Majdak, Piotr Laboratoire des systèmes perceptifs Département d'études cognitives École Normale Supérieure PSL University CNRS Paris France Departments of Biomedical Engineering and Neuroscience University of Rochester RochesterNY United States Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmark Department of Electrical and Computer Engineering McMaster University HamiltonON Canada Hearing Technology group WAVES Department of Information Technology Ghent University Ghent Belgium Acoustics Research Institute Austrian Academy of Sciences Vienna Austria

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models. © 2021, CC BY-NC-SA.

关键词： Physiological models

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

arXiv

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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

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Corrigendum to “Vibration of mechanically-assembled 3D microstructures formed by compressive buckling” [Journal of the Mechanics and Physics of Solids 112 (2018) 187–208]

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Journal of the Mechanics and Physics of Solids 2022年 158卷

作者： Heling Wang Xin Ning Haibo Li Haiwen Luan Yeguang Xue Xinge Yu Zhichao Fan Luming Li John A. Rogers Yihui Zhang Yonggang Huang Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA AML Department of Engineering Mechanics Tsinghua University Beijing 100084 China Center for Mechanics and Materials and Center for Flexible Electronics Technology Tsinghua University Beijing 100084 China Man-machine-Environment Engineering Institute Department of Aeronautics & Astronautics Engineering Tsinghua University Beijing 100084 China Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science Neurological Surgery Center for Bio-Integrated Electronics Simpson Querrey Institute for BioNanotechnology McCormick School of Engineering and Feinberg School of Medicine Northwestern University Evanston Illinois 60208 USA

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Author Correction: A wireless haptic interface for programmable patterns of touch across large areas of the skin

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Nature Electronics 2022年 621页

作者： Yei Hwan Jung Jaeman Lim Jae-Young Yoo Abraham Vázquez-Guardado Jae-Hwan Kim Jin-Tae Kim Haiwen Luan Minsu Park Hee-Sup Shin Chun-Ju Su Robert Schloen Jacob Trueb Jan-Kai Chang Da Som Yang Yoonseok Park Hanjun Ryu Hong-Joon Yoon Geumbee Lee Hyoyeong Jeong Jong Uk Kim John A. Rogers Raudel Avila Yonggang Huang Aadeel Akhtar Jesse Cornman Tae-il Kim Department of Electronic Engineering Hanyang University Seoul Republic of Korea Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL USA Department of Mechanical Engineering Northwestern University Evanston IL USA Department of Materials Science and Engineering Northwestern University Evanston IL USA Departments of Biomedical Engineering Neurological Surgery Chemistry and Electrical and Computer Engineering Northwestern University Evanston IL USA Department of Civil and Environmental Engineering Northwestern University Evanston IL USA PSYONIC Champaign IL USA School of Chemical Engineering Sungkyunkwan University (SKKU) Suwon Republic of Korea

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Assessing depression recurrence, cognitive burden, and neurobiological homeostasis in late life: Design and rationale of the REMBRANDT Study

Journal of mood and anxiety disorders

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Journal of mood and anxiety disorders 2024年 5卷 100038页

作者： Warren D Taylor Olusola Ajilore Helmet T Karim Meryl A Butters Robert Krafty Brian D Boyd Layla Banihashemi Sarah M Szymkowicz Claire Ryan Jason Hassenstab Bennett A Landman Carmen Andreescu Center for Cognitive Medicine Department of Psychiatry and Behavioral Science Vanderbilt University Medical Center Nashville TN. Geriatric Research Education and Clinical Center Veterans Affairs Tennessee Valley Health System Nashville TN. Department of Psychiatry University of Illinois-Chicago Chicago IL. Department of Psychiatry University of Pittsburgh Medical Center Pittsburgh PA. Department of Biostatistics and Bioinformatics Emory University Atlanta GA. Departments of Neurology and Psychiatry Washington University in St. Louis St. Louis MO. Departments of Computer Science Electrical Engineering and Biomedical Engineering Vanderbilt University Department of Radiology and Radiological Sciences Vanderbilt University Medical Center.

Background:Late-life depression is characterized by disability, cognitive impairment and decline, and a high risk of recurrence following remission. Aside from past psychiatric history, prognostic neurobiological and clinical factors influencing recurrence risk are unclear. Moreover, it is unclear if cognitive impairment predisposes to recurrence, or whether recurrent episodes may accelerate brain aging and cognitive decline. The purpose of the REMBRANDT study (Recurrence markers, cognitive burden, and neurobiological homeostasis in late-life depression) is to better elucidate these relationships and identify phenotypic, cognitive, environmental, and neurobiological factors contributing to and predictive of depression recurrence. Methods:Across three sites, REMBRANDT will enroll 300 depressed elders who will receive antidepressant treatment. The goal is to enroll 210 remitted depressed participants and 75 participants with no mental health history into a two-year longitudinal phase focusing on depression recurrence. Participants are evaluated every 2 months with deeper assessments occurring every 8 months, including structural and functional neuroimaging, environmental stress assessments, deep symptom phenotyping, and two weeks of 'burst' ecological momentary assessments to elucidate variability in symptoms and cognitive performance. A broad neuropsychological test battery is completed at the beginning and end of the longitudinal study. Significance:REMBRANDT will improve our understanding of how alterations in neural circuits and cognition that persist during remission contribute to depression recurrence vulnerability. It will also elucidate how these processes may contribute to cognitive impairment and decline. This project will obtain deep phenotypic data that will help identify vulnerability and resilience factors that can help stratify individual clinical risk.

关键词： aging cognition depression longitudinal design methods study design

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Work in progress: Project tadpole - A student-led engineering service club 125

Work in progress: Project tadpole - A student-led engineerin...

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125th ASEE Annual Conference and Exposition

作者： Li, Martin Loomis, Brianna Caves, Kevin Department of Biomedical and Electrical and Computer Engineering Duke University United States Pratt School of Engineering Duke University Departments of Surgery and Medicine United States

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Correction to "Scalable Inhibitors of the Nsp3-Nsp4 Coupling in SARS-CoV-2"

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ACS omega 2024年第34期9卷 36857页

作者： Abdul-Rahman Azizogli Varun Pai Francesco Coppola Roya Jafari Joseph B Dodd-O Rohan Harish Bhavani Balasubramanian Jatin Kashyap Amanda M Acevedo-Jake Petr Kral Vivek A Kumar Department of Biological Sciences New Jersey Institute of Technology Newark New Jersey 07102 United States. Department of Chemistry University of Illinois at Chicago Chicago Illinois 60607 United States. Department of Biomedical Engineering New Jersey Institute of Technology Newark New Jersey 07102 United States. Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark New Jersey 07102 United States. Department of Chemistry and Environmental Sciences New Jersey Institute of Technology Newark New Jersey 07102 United States. Department of Chemistry and Departments of Physics Pharmaceutical Sciences and Chemical Engineering University of Illinois at Chicago Chicago Illinois 60607 United States. Department of Biological Sciences Department of Biomedical Engineering and Department of Chemical and Materials Engineering New Jersey Institute of Technology Newark New Jersey 07102 United States. Department of Endodontics Rutgers School of Dental Medicine Newark New Jersey 07103 United States.

[This corrects the article DOI: 10.1021/acsomega.2c06384.].

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