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arXiv 2024年

作者： Loutfi, Mahdi Ait Lhaj Podasca, Teodora Boblea Zwanenburg, Alex Upadhaya, Taman Ginart, Jorge Barrios Raleigh, David R. Chen, William C. Capaldi, Dante P.I. Zheng, Hong Gevaert, Olivier Wu, Jing Silva, Alvin C. Zhang, Paul J. Bai, Harrison X. Seuntjens, Jan Löck, Steffen Richard, Patrick O. Morin, Olivier Reinhold, Caroline Lepage, Martin Vallières, Martin Department of Computer Science Université de Sherbrooke SherbrookeQC Canada Department of Surgery Service of Urology Université de Sherbrooke SherbrookeQC Canada OncoRay National Center for Radiation Research in Oncology Faculty of Medicine University Hospital Carl Gustav Carus TUD Dresden University of Technology Helmholtz-Zentrum Dresden-Rossendorf Dresden Germany Germany Heidelberg Germany Faculty of Medicine University Hospital Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Dresden Germany Department of Radiation Oncology Cedars-Sinai Medical Center Los AngelesCA United States Department of Radiation Oncology University of California San Francisco San FranciscoCA United States Departments of Radiation Oncology Neurological Surgery and Pathology University of California San Francisco San FranciscoCA United States Center for Biomedical Informatics Research School of Medicine Stanford University CA94305 United States Department of Medicine Department of Biomedical Data Science Stanford University StanfordCA94305 United States Department of Radiology The Second Xiangya Hospital of Central South University Hunan Changsha410011 China Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd. ScottsdaleAZ85259 United States Department of Pathology and Clinical Medicine Hospital of the University of Pennsylvania PhiladelphiaPA United States Department of Radiology and Radiological Sciences Johns Hopkins BaltimoreMD United States Princess Margaret Cancer Centre University Health Network Departments of Radiation Oncology & Medical Biophysics University of Toronto TorontoON Canada Division of Urology Centre Hospitalier Universitaire de Sherbrooke Université de Sherbrooke Cancer Research Institute SherbrookeQC Canada The Research Institute The McGill University Health Center MontrealQC Canada Département de Médecine Nucléaire et Radiobiologie Université de Sherbrooke SherbrookeQC Canada Centre d’imager

Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: To establish a methodology and provide tools for identifying and explaining the smallest set of predictive features radiomic features. Materials and Methods: Radiomic features (a total of 89,714) were extracted from five distinct datasets with different cancer types: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). These features were categorized into complexity levels, defined by the number of computational steps required for their computation, encompassing morphological, intensity, texture, linear filters-based, and nonlinear filter-based features. For every dataset, models were trained on each complexity level specifically to classify clinical outcomes, and their performance was evaluated using the area under the curve (AUC). The most informative features were identified and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was designed and implemented to streamline radiomic studies through both code-based and graphical-based approaches, and was applied using our proposed methodology to analyze the datasets. Morphological features were found to be optimal in two cases: for MRI-based meningioma (AUC: 0.65;sensitivity: 64%;specificity: 62%;95% CI: 0.59, 0.72) and MRI-based low-grade glioma (AUC: 0.68;sensitivity: 68%;specificity: 69%;95% CI: 0.60, 0.75). Additionally, intensity features were optimal in two instances: for contras

关键词： Classification (of information)

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Learning to play with intrinsically-motivated, self-aware agents 32

Learning to play with intrinsically-motivated, self-aware ag...

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32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Haber, Nick Mrowca, Damian Wang, Stephanie Fei-Fei, Li Yamins, Daniel L.K. Departments of Psychology StanfordCA94305 United States Pediatrics Stanford CA94305 United States Biomedical Data Science StanfordCA94305 United States Computer Science StanfordCA94305 United States Wu Tsai Neurosciences Institute StanfordCA94305 United States

Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which an agent can move and interact with objects it sees, we propose a "world-model" network that learns to predict the dynamic consequences of the agent's actions. Simultaneously, we train a separate explicit "self-model" that allows the agent to track the error map of its world-model. It then uses the self-model to adversarially challenge the developing world-model. We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering. Moreover, the world-model that the agent learns supports improved performance on object dynamics prediction, detection, localization and recognition tasks. Taken together, our results are initial steps toward creating flexible autonomous agents that self-supervise in realistic physical environments. © 2018 Curran Associates *** rights reserved.

关键词： Motion estimation

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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Reply to Response to Vacuous standards - Subversion of the OSAC standards-development process

Forensic science international. Synergy

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Forensic science international. Synergy 2021年 3卷 100149页

作者： Geoffrey Stewart Morrison Cedric Neumann Patrick Henry Geoghegan Gary Edmond Tim Grant R Brent Ostrum Paul Roberts Michael Saks Denise Syndercombe Court William C Thompson Sandy Zabell Forensic Data Science Laboratory & Forensic Speech Science Laboratory Computer Science Department & Aston Institute for Forensic Linguistics Aston University Birmingham UK. Forensic Evaluation Ltd Birmingham UK. Two N's Forensics Inc Brookings SD USA. Department of Biomedical Engineering Aston University Birmingham UK. Forensic Data Science Laboratory Computer Science Department & Aston Institute for Forensic Linguistics Aston University Birmingham UK. Faculty of Law and Justice University of New South Wales Sydney NSW Australia. Aston Institute for Forensic Linguistics Aston University Birmingham UK. Forensic Document Examination Section Science and Engineering Directorate Canada Border Services Agency Ottawa ON Canada. School of Law University of Nottingham Nottingham UK. China University of Political Science and Law Beijing China. Sandra Day O'Connor College of Law Department of Psychology And Center for Law Science & Innovation Arizona State University Phoenix AZ USA. King's Forensics King's College London London UK. Departments of Criminology and Psychology and School of Law University of California Irvine CA USA. Departments of Mathematics and Statistics Northwestern University Evanston IL USA.

This Letter to the Editor is a reply to Mohammed et al. (2021) https://***/10.1016/***.2021.100145, which in turn is a response to Morrison et al. (2020) "Vacuous standards - subversion of the OSAC standards-deve... 详细信息

关键词： Forensic science Method validation Quality management Standard Validation

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Recurrence quantification analysis of dynamic brain networks

arXiv

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arXiv 2020年

作者： Lopes, Marinho A. Zhang, Jiaxiang Krzemiński, Dominik Hamandi, Khalid Chen, Qi Livi, Lorenzo Masuda, Naoki Department of Engineering Mathematics University of Bristol BS8 1UB United Kingdom Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University CardiffCF24 4HQ United Kingdom Center for Studies of Psychological Application School of Psychology South China Normal University Guangzhou510631 China Departments of Computer Science and Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom Department of Mathematics University at Buffalo State University of New York United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York United States

Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in brain function in health and other neurological disorders. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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Artificial intelligence-based image classification for diagnosis of skin cancer: Challenges and opportunities

arXiv

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arXiv 2019年

作者： Goyal, Manu Knackstedt, Thomas Yan, Shaofeng Oakley, Amanda Hassanpour, Saeed Department of Biomedical Data Science Dartmouth College HanoverNH United States Department of Dermatology Metrohealth System and School of Medicine Case Western Reserve University ClevelandOH United States Section of Dermatopathology Department of Pathology and Laboratory Medicine Dartmouth-Hitchcock Medical Center Geisel School of Medicine Dartmouth College HanoverNH United States Waikato Clinical School University of Auckland Hamilton New Zealand Departments of Biomedical Data Science Computer Science and Epidemiology Dartmouth College HanoverNH United States

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI-based image classification solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some Copyright © 2019, The Authors. All rights reserved.

关键词： Dermatology

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Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder

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BMC medicine 2023年第1期21卷 113页

作者： Shile Qi Vince D Calhoun Daoqiang Zhang Jeremy Miller Zhi-De Deng Katherine L Narr Yvette Sheline Shawn M McClintock Rongtao Jiang Xiao Yang Joel Upston Tom Jones Jing Sui Christopher C Abbott College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. shile.qi@***. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. Department of Psychiatry University of New Mexico Albuquerque NM USA. Noninvasive Neuromodulation Unit Experimental Therapeutics & Pathophysiology Branch National Institute of Mental Health Bethesda MD USA. Departments of Neurology Psychiatry and Biobehavioral Sciences University of California Los Angeles CA USA. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Division of Psychology Department of Psychiatry UT Southwestern Medical Center Dallas TX USA. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Huaxi Brain Research Center West China Hospital of Sichuan University Chengdu China. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. jsui@***. Department of Psychiatry University of New Mexico Albuquerque NM USA. cabbott@salud.unm.edu.

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Author Correction: Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach

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NPJ digital medicine 2024年第1期7卷 93页

作者： Rohaid Ali Ian D Connolly Oliver Y Tang Fatima N Mirza Benjamin Johnston Hael F Abdulrazeq Rachel K Lim Paul F Galamaga Tiffany J Libby Neel R Sodha Michael W Groff Ziya L Gokaslan Albert E Telfeian John H Shin Wael F Asaad James Zou Curtis E Doberstein Department of Neurosurgery Rhode Island Hospital and The Warren Alpert Medical School of Brown University Providence RI USA. ali.rohaid@***. Norman Prince Neurosciences Institute Providence RI USA. ali.rohaid@***. Department of Neurosurgery Massachusetts General Hospital Boston MA USA. Department of Neurosurgery Rhode Island Hospital and The Warren Alpert Medical School of Brown University Providence RI USA. Department of Dermatology The Warren Alpert Medical School of Brown University Providence RI USA. Department of Neurosurgery Brigham and Women's Hospital Boston MA USA. Norman Prince Neurosciences Institute Providence RI USA. Department of Surgery & Division of Cardiothoracic Surgery Rhode Island Hospital and The Warren Alpert Medical School of Brown University Providence RI USA. Ratcliffe Harten Galamaga LLP Providence RI USA. Departments of Electrical Engineering Biomedical Data Science and Computer Science Stanford University Stanford CA USA. Chan Zuckerberg Biohub San Francisco CA USA.

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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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Infectious disease surveillance needs for the United States: lessons from COVID-19

arXiv

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arXiv 2023年

作者： Lipsitch, Marc Bassett, Mary T. Brownstein, John S. Elliott, Paul Eyre, David Grabowski, M. Kate Hay, James A. Johansson, Michael Kissler, Stephen M. Larremore, Daniel B. Layden, Jennifer Lessler, Justin Lynfield, Ruth MacCannell, Duncan Madoff, Lawrence C. Metcalf, Jessica E. Meyers, Lauren A. Ofori, Sylvia K. Quinn, Celia Ramos Bento, Ana I. Reich, Nick Riley, Steven Rosenfeld, Roni Samore, Matthew H. Sampath, Rangarajan Slayton, Rachel B. Swerdlow, David L. Truelove, Shaun Varma, Jay K. Grad, Yonatan H. Center for Forecasting and Outbreak Analytics US Centers for Disease Control and Prevention AtlantaGA United States Center for Communicable Disease Dynamics Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States François-Xavier Bagnoud Center for Health and Human Rights Department of Social and Behavioral Sciences The Harvard T.H. Chan School of Public Health BostonMA United States Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Epidemiology and Public Health Medicine Imperial College London London United Kingdom Big Data Institute Nuffield Department of Population Health University of Oxford Oxford United Kingdom Department of Epidemiology Bloomberg School of Public Health Johns Hopkins University BaltimoreMD United States Division of Vector-Borne Diseases US Centers for Disease Control and Prevention San JuanPR United States Department of Computer Science University of Colorado Boulder BoulderCO United States BioFrontiers Institute University of Colorado Boulder BoulderCO United States Office of Public Health Data Surveillance and Technology US Centers for Disease Control and Prevention AtlantaGA United States Department of Epidemiology UNC Gillings School of Public Health Chapel HillNC United States Minnesota Department of Health MinneapolisMN United States Office of Advanced Molecular Detection US Centers for Disease Control and Prevention AtlantaGA United States Massachusetts Department of Public Health BostonMA United States Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Department of Integrative Biology University of Texas at Austin AustinTX United States Division of Disease Control New York City Department of Health and Mental Hygiene New York CityNY United States Pandemic Prevention Initiative The Rockefeller Foundation WashingtonDC United States Department of Biostatistics University of Massachusetts Amh

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity. © 2023, CC BY.

关键词： COVID-19

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