Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Jaron, Chong"'
Autor:
Adrian P. Brady, Bibb Allen, Jaron Chong, Elmar Kotter, Nina Kottler, John Mongan, Lauren Oakden-Rayner, Daniel Pinto dos Santos, An Tang, Christoph Wald, John Slavotinek
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-19 (2024)
Abstract Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by a
Externí odkaz:
https://doaj.org/article/a0270d79cf654afb80e58b7ac7183bff
Autor:
Jimmy M. Hsu, Aaron Hass, Marc-Alexandre Gingras, Jaron Chong, Cecilia Costiniuk, Nicole Ezer, Richard S. Fraser, Emily G. McDonald, Todd C. Lee
Publikováno v:
BMC Infectious Diseases, Vol 20, Iss 1, Pp 1-5 (2020)
Abstract Background Pneumocystis jirovecii pneumonia (PJP) can be challenging to diagnose, often requiring bronchoscopy. Since most patients suspected of PJP undergo imaging, we hypothesized that the findings of these studies could help estimate the
Externí odkaz:
https://doaj.org/article/47f9dfb4e1c14879944e69c9ba3c745b
Autor:
Jimmy Tat, M.D., M.Sc., Jordan Crawford, Jaron Chong, M.D., Tom Powell, M.D., Thomas G. Fevens, Ph.D., Tiberiu Popa, Ph.D., Paul A. Martineau, M.D.
Publikováno v:
Arthroscopy, Sports Medicine, and Rehabilitation, Vol 3, Iss 1, Pp e89-e96 (2021)
Purpose: To dynamically assess for Hill–Sachs engagement with animated 3-dimensional (3D) shoulder models. Methods: We created 3D shoulder models from reconstructed computed tomography (CT) images from a consecutive series of patients with recurren
Externí odkaz:
https://doaj.org/article/cc753f44f0664b9fa74e81e52d403b51
Autor:
Natalia Gorelik, Yousef Darwish, William R. Walter, Christopher J. Burke, Dost Sarpel, Jaron Chong, Ronald S. Adler
Publikováno v:
European Radiology. 32:6759-6768
Publikováno v:
Medical Imaging 2023: Physics of Medical Imaging.
Publikováno v:
Medical Imaging 2023: Image Processing.
Autor:
William Tanguay, Philippe Acar, Benjamin Fine, Mohamed Abdolell, Bo Gong, Alexandre Cadrin-Chênevert, Carl Chartrand-Lefebvre, Jean Chalaoui, Andrei Gorgos, Anne Shu-Lei Chin, Julie Prénovault, François Guilbert, Laurent Létourneau-Guillon, Jaron Chong, An Tang
Publikováno v:
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.
Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in
Autor:
Jaron Chong, Thomas Fevens, Paul A. Martineau, Tom Powell, Tiberiu Popa, Jimmy Tat, Jordan Crawford
Publikováno v:
Arthroscopy, Sports Medicine, and Rehabilitation, Vol 3, Iss 1, Pp e89-e96 (2021)
Arthroscopy, Sports Medicine, and Rehabilitation
Arthroscopy, Sports Medicine, and Rehabilitation
Purpose To dynamically assess for Hill–Sachs engagement with animated 3-dimensional (3D) shoulder models. Methods We created 3D shoulder models from reconstructed computed tomography (CT) images from a consecutive series of patients with recurrent
CT features associated with underlying malignancy in patients with diagnosed mesenteric panniculitis
Autor:
Jules Grégory, Jérémy Dana, Isaac Yang, Jaron Chong, Louis Drevon, Maxime Ronot, Valérie Vilgrain, Caroline Reinhold, Benoît Gallix
Publikováno v:
Diagnostic and interventional imaging. 103(9)
The purpose of this study was to identify abdominal computed tomography (CT) features associated with underlying malignancy in patients with mesenteric panniculitis (MP).This single-institution retrospective longitudinal cohort study included patient
Autor:
Jaron Chong, Jack W. Luo
Publikováno v:
Neuroimaging Clinics of North America. 30:447-458
Natural language processing (NLP) is an interdisciplinary field, combining linguistics, computer science, and artificial intelligence to enable machines to read and understand human language for meaningful purposes. Recent advancements in deep learni