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arXiv 2022年

作者： Ferreira, Danielle L. Lau, Connor Salaymang, Zaynaf Arnaout, Rima Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute University of California San Francisco 521 Parnassus Avenue San FranciscoCA94143 United States Department of Medicine Division of Cardiology 505 Parnassus Avenue San FranciscoCA94143 United States Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute UCSF-UC Berkeley Joint Program in Computational Precision Health Department of Radiology Center for Intelligent Imaging University of California San Francisco 521 Parnassus Ave Box 0124 San FranciscoCA94143 United States

Segmentation and measurement of cardiac chambers is critical in cardiac ultrasound but is laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same laborious manual annotations. We built a pipeline for self-supervised (no manual labels) segmentation combining computer vision, clinical domain knowledge, and deep learning. We trained on 450 echocardiograms (93,000 images) and tested on 8,393 echocardiograms (4,476,266 images;mean 61 years, 51% female), using the resulting segmentations to calculate biometrics. We also tested against external images from an additional 10,030 patients with available manual tracings of the left ventricle. r2 between clinically measured and pipeline-predicted measurements were similar to reported inter-clinician variation and comparable to supervised learning across several different measurements (r2 0.56-0.84). Average accuracy for detecting abnormal chamber size and function was 0.85 (range 0.71-0.97) compared to clinical measurements. A subset of test echocardiograms (n=553) had corresponding cardiac MRIs, where MRI is the gold standard. Correlation between pipeline and MRI measurements was similar to that between clinical echocardiogram and MRI. Finally, the pipeline accurately segments the left ventricle with an average Dice score of 0.89 (95% CI [0.89]) in the external, manually labeled dataset. Our results demonstrate a manual-label free, clinically valid, and highly scalable method for segmentation from ultrasound, a noisy but globally important imaging modality. © 2022, CC BY-NC-ND.

关键词： Self-supervised learning

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Generative AI in Medicine

arXiv

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arXiv 2024年

作者： Shanmugam, Divya Agrawal, Monica Movva, Rajiv Chen, Irene Y. Ghassemi, Marzyeh Jacobs, Maia Pierson, Emma Department of Computer Science Cornell Tech New York10044 United States Department of Biostatistics and Bioinformatics Duke University Durham27705 United States Department of Computer Science Duke University Durham27708 United States Department of Computational Precision Health UC Berkeley UCSF Berkeley94709 United States Department of Electrical Engineering and Computer Science Berkeley AI Research Berkeley94709 United States Department of Electrical Engineering and Computer Science Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge02139 United States Department of Computer Science Northwestern University Evanston60208 United States Department of Preventive Medicine Northwestern University Evanston60208 United States Department of Population Health Sciences Weill Cornell Medical College New York10044 United States

The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges – including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models – which must be overcome to realize this potential, and the open research directions they give rise to. © 2024, CC BY.

关键词： Generative adversarial networks

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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The Science of precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular health Disparities

JACC: Advances

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JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

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Author Correction: lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements

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Nature protocols 2021年第7期16卷 3736页

作者： M Grace Gordon Fumitaka Inoue Beth Martin Max Schubach Vikram Agarwal Sean Whalen Shiyun Feng Jingjing Zhao Tal Ashuach Ryan Ziffra Anat Kreimer Ilias Georgakopoulos-Soares Nir Yosef Chun Jimmie Ye Katherine S Pollard Jay Shendure Martin Kircher Nadav Ahituv Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. Institute for Human Genetics University of California San Francisco San Francisco CA USA. Biological and Medical Informatics Graduate Program University of California San Francisco San Francisco CA USA. Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. fumitaka.inoue@ucsf.edu. Institute for Human Genetics University of California San Francisco San Francisco CA USA. fumitaka.inoue@ucsf.edu. Berlin Institute of Health (BIH) Berlin Germany. Department of Genome Sciences University of Washington Seattle WA USA. Calico Life Sciences LLC South San Francisco CA USA. Gladstone Institutes San Francisco CA USA. Department of Electrical Engineering and Computer Sciences and Center for Computational Biology University of California Berkeley Berkeley CA USA. Chan-Zuckerberg Biohub San Francisco CA USA. Division of Rheumatology Department of Medicine University of California San Francisco San Francisco CA USA. Institute for Computational Health Sciences University of California San Francisco San Francisco California USA. Department of Epidemiology and Biostatistics and Institute of Computational Health Sciences University of California San Francisco San Francisco CA USA. Department of Genome Sciences University of Washington Seattle WA USA. shendure@uw.edu. Howard Hughes Medical Institute Seattle WA USA. shendure@uw.edu. Brotman Baty Institute for Precision Medicine University of Washington Seattle WA USA. shendure@uw.edu. Berlin Institute of Health (BIH) Berlin Germany. martin.kircher@bihealth.de. Charité-Universitätsmedizin Berlin Berlin Germany. martin.kircher@bihealth.de. Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. nadav.ahituv@ucsf.edu. Institute for Human Genetics University of California San

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