Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Christopher P Bridge"'
Autor:
Deepa Krishnaswamy, Dennis Bontempi, Vamsi Krishna Thiriveedhi, Davide Punzo, David Clunie, Christopher P. Bridge, Hugo J. W. L. Aerts, Ron Kikinis, Andrey Fedorov
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence
Externí odkaz:
https://doaj.org/article/dcc71e61a06943b383830340b580a81d
Autor:
Romane Gauriau, Bernardo C. Bizzo, Donnella S. Comeau, James M. Hillis, Christopher P. Bridge, John K. Chin, Jayashri Pawar, Ali Pourvaziri, Ivana Sesic, Elshaimaa Sharaf, Jinjin Cao, Flavia T. C. Noro, Walter F. Wiggins, M. Travis Caton, Felipe Kitamura, Keith J. Dreyer, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Ramon G. Gonzalez, Michael H. Lev
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Non-contrast head CT (NCCT) is extremely insensitive for early ( 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-pos
Externí odkaz:
https://doaj.org/article/854bbf674fbe487dae58da0a4762c5f1
Autor:
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Brian Befano, Silvia De Sanjosé, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer
Publikováno v:
npj Digital Medicine, Vol 5, Iss 1, Pp 1-11 (2022)
Abstract The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Ideal repeatable models output predictions without variation during independent tes
Externí odkaz:
https://doaj.org/article/d201db3d55dc4827b2c9d94495d6e5ca
Autor:
Satvik Tripathi, Azadeh Tabari, Arian Mansur, Harika Dabbara, Christopher P. Bridge, Dania Daye
Publikováno v:
Diagnostics, Vol 14, Iss 2, p 174 (2024)
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improve
Externí odkaz:
https://doaj.org/article/3a1f0b0f45424a4f874acdec0e65d82f
Autor:
Christopher P. Bridge, Bernardo C. Bizzo, James M. Hillis, John K. Chin, Donnella S. Comeau, Romane Gauriau, Fabiola Macruz, Jayashri Pawar, Flavia T. C. Noro, Elshaimaa Sharaf, Marcelo Straus Takahashi, Bradley Wright, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Stefano Pedemonte, R. Gilberto González
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learnin
Externí odkaz:
https://doaj.org/article/157c664d20a9403091eb001175b7373f
Autor:
Kirti, Magudia, Christopher P, Bridge, Camden P, Bay, Subrina, Farah, Ana, Babic, Florian J, Fintelmann, Lauren K, Brais, Katherine P, Andriole, Brian M, Wolpin, Michael H, Rosenthal
Publikováno v:
American Journal of Roentgenology. 220:236-244
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29b2630dba0dffdf7a6b51819207b437
https://doi.org/10.2214/ajr.22.27977
https://doi.org/10.2214/ajr.22.27977
Autor:
Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann
Publikováno v:
Journal of Digital Imaging. 35:1719-1737
Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c80fb9c5218ff14bdcf7c55ba4900c75
https://doi.org/10.1007/s10278-022-00683-y
https://doi.org/10.1007/s10278-022-00683-y
Autor:
Romane Gauriau, John K Chin, Christopher P. Bridge, James Hillis, Bradley Wright, Elshaimaa Sharaf, Jayashri Pawar, R. Gilberto Gonzalez, Stefano Pedemonte, John Francis Kalafut, Bernardo Bizzo, Stuart R. Pomerantz, Katherine P. Andriole, Fabiola B. C. Macruz, Marcelo Straus Takahashi, Donnella S Comeau, Flavia T C Noro
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
BackgroundStroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. MethodsWe developed a machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03e4e048dcd8c86260633466ae7a316c
https://doaj.org/article/157c664d20a9403091eb001175b7373f
https://doaj.org/article/157c664d20a9403091eb001175b7373f
Autor:
Cory Robinson-Weiss, Jay Patel, Bernardo C. Bizzo, Daniel I. Glazer, Christopher P. Bridge, Katherine P. Andriole, Borna Dabiri, John K. Chin, Keith Dreyer, Jayashree Kalpathy-Cramer, William W. Mayo-Smith
Publikováno v:
Radiology. 306
Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contrast-enhanced CT images and classify glands as normal or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f7933d49cc84effd2c5c055a3dca773
https://doi.org/10.1148/radiol.220101
https://doi.org/10.1148/radiol.220101
Autor:
James M Hillis, Bernardo C Bizzo, Romane Gauriau, Christopher P Bridge, John K Chin, Buthaina Hakamy, Sarah Mercaldo, John Conklin, Sayon Dutta, William A Mehan, Robert W Regenhardt, Ajay Singh, Aneesh B Singhal, Jonathan D Sonis, Marc D Succi, Tianhao Zhang, Bin Xing, John F Kalafut, Keith J Dreyer, Michael H Lev, R Gilberto González
Acute ischemic stroke can be subtle to detect on non-contrast computed tomography imaging. We show that a novel artificial intelligence model significantly improves the performance of physicians, including ED physicians, neurologists and radiologists
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cd5f830fad6f0074469014b57efa9aa
https://doi.org/10.1101/2023.01.16.23284632
https://doi.org/10.1101/2023.01.16.23284632
