Zobrazeno 1 - 10 of 1 902 pro vyhledávání: '"Fayad, Zahi A"'
1
Akademický článek
Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study
Autor: Hirten, Robert P, Danieletto, Matteo, Tomalin, Lewis, Choi, Katie Hyewon, Zweig, Micol, Golden, Eddye, Kaur, Sparshdeep, Helmus, Drew, Biello, Anthony, Pyzik, Renata, Charney, Alexander, Miotto, Riccardo, Glicksberg, Benjamin S, Levin, Matthew, Nabeel, Ismail, Aberg, Judith, Reich, David, Charney, Dennis, Bottinger, Erwin P, Keefer, Laurie, Suarez-Farinas, Mayte, Nadkarni, Girish N, Fayad, Zahi A
Publikováno v: Journal of Medical Internet Research, Vol 23, Iss 2, p e26107 (2021)
BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. ObjectiveWe performed an evaluation of HRV collected by a w
Externí odkaz: https://doaj.org/article/d7e0647c8f7b41488bc32f8da0f9db33
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2
Akademický článek
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach
Autor: Vaid, Akhil, Jaladanki, Suraj K, Xu, Jie, Teng, Shelly, Kumar, Arvind, Lee, Samuel, Somani, Sulaiman, Paranjpe, Ishan, De Freitas, Jessica K, Wanyan, Tingyi, Johnson, Kipp W, Bicak, Mesude, Klang, Eyal, Kwon, Young Joon, Costa, Anthony, Zhao, Shan, Miotto, Riccardo, Charney, Alexander W, Böttinger, Erwin, Fayad, Zahi A, Nadkarni, Girish N, Wang, Fei, Glicksberg, Benjamin S
Publikováno v: JMIR Medical Informatics, Vol 9, Iss 1, p e24207 (2021)
BackgroundMachine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Ob
Externí odkaz: https://doaj.org/article/11f1e0ca04304420af2f5d46261fc8ee
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4
Report
VISION-MAE: A Foundation Model for Medical Image Segmentation and Classification
Autor: Liu, Zelong, Tieu, Andrew, Patel, Nikhil, Zhou, Alexander, Soultanidis, George, Fayad, Zahi A., Deyer, Timothy, Mei, Xueyan
Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, an
Externí odkaz: http://arxiv.org/abs/2402.01034
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5
Report
MRAnnotator: A Multi-Anatomy Deep Learning Model for MRI Segmentation
Autor: Zhou, Alexander, Liu, Zelong, Tieu, Andrew, Patel, Nikhil, Sun, Sean, Yang, Anthony, Choi, Peter, Fauveau, Valentin, Soultanidis, George, Huang, Mingqian, Doshi, Amish, Fayad, Zahi A., Deyer, Timothy, Mei, Xueyan
Purpose To develop a deep learning model for multi-anatomy and many-class segmentation of diverse anatomic structures on MRI imaging. Materials and Methods In this retrospective study, two datasets were curated and annotated for model development and
Externí odkaz: http://arxiv.org/abs/2402.01031
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6
Report
RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging
Autor: Liu, Zelong, Zhou, Alexander, Yang, Arnold, Yilmaz, Alara, Yoo, Maxwell, Sullivan, Mikey, Zhang, Catherine, Grant, James, Li, Daiqing, Fayad, Zahi A., Huver, Sean, Deyer, Timothy, Mei, Xueyan
Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creat
Externí odkaz: http://arxiv.org/abs/2312.05953
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