Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Christopher G. Filippi"'
Autor:
Emily W. Avery, Anthony Abou-Karam, Sandra Abi-Fadel, Jonas Behland, Adrian Mak, Stefan P. Haider, Tal Zeevi, Pina C. Sanelli, Christopher G. Filippi, Ajay Malhotra, Charles C. Matouk, Guido J. Falcone, Nils Petersen, Lauren H. Sansing, Kevin N. Sheth, Seyedmehdi Payabvash
Publikováno v:
Diagnostics, Vol 14, Iss 5, p 485 (2024)
Background: A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO coll
Externí odkaz:
https://doaj.org/article/a9ca0c7c749943ccad13131170b9a977
Autor:
Emily W. Avery, Jonas Behland, Adrian Mak, Stefan P. Haider, Tal Zeevi, Pina C. Sanelli, Christopher G. Filippi, Ajay Malhotra, Charles C. Matouk, Christoph J. Griessenauer, Ramin Zand, Philipp Hendrix, Vida Abedi, Guido J. Falcone, Nils Petersen, Lauren H. Sansing, Kevin N. Sheth, Seyedmehdi Payabvash
Publikováno v:
Data in Brief, Vol 44, Iss , Pp 108542- (2022)
With advances in high-throughput image processing technologies and increasing availability of medical mega-data, the growing field of radiomics opened the door for quantitative analysis of medical images for prediction of clinically relevant informat
Externí odkaz:
https://doaj.org/article/8430a8e0e57141a99374e4391ab71ab6
Autor:
Emily W. Avery, Jonas Behland, Adrian Mak, Stefan P. Haider, Tal Zeevi, Pina C. Sanelli, Christopher G. Filippi, Ajay Malhotra, Charles C. Matouk, Christoph J. Griessenauer, Ramin Zand, Philipp Hendrix, Vida Abedi, Guido J. Falcone, Nils Petersen, Lauren H. Sansing, Kevin N. Sheth, Seyedmehdi Payabvash
Publikováno v:
NeuroImage: Clinical, Vol 34, Iss , Pp 103034- (2022)
Background and Purpose: As “time is brain” in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool fo
Externí odkaz:
https://doaj.org/article/bb4bf1af3c8544edbf1615d58e844010
Autor:
Michelle D. Bardis, Roozbeh Houshyar, Peter D. Chang, Alexander Ushinsky, Justin Glavis-Bloom, Chantal Chahine, Thanh-Lan Bui, Mark Rupasinghe, Christopher G. Filippi, Daniel S. Chow
Publikováno v:
Cancers, Vol 12, Iss 5, p 1204 (2020)
Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML),
Externí odkaz:
https://doaj.org/article/335c178b4719492cb2d2ff5840272df2
Autor:
Madeleine M. Shaver, Paul A. Kohanteb, Catherine Chiou, Michelle D. Bardis, Chanon Chantaduly, Daniela Bota, Christopher G. Filippi, Brent Weinberg, Jack Grinband, Daniel S. Chow, Peter D. Chang
Publikováno v:
Cancers, Vol 11, Iss 6, p 829 (2019)
Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular prolifera
Externí odkaz:
https://doaj.org/article/00ed358613694ee1ad66cba329beddca
Publikováno v:
Behavior Genetics. 53:208-218
Using baseline (ages 9–10) and two-year follow-up (ages 11–12) data from monozygotic and dizygotic twins enrolled in the longitudinal Adolescent Brain Cognitive DevelopmentSM Study, we investigated the genetic and environmental contributions to m
Publikováno v:
Neuroimaging Clinics of North America. 33:69-82
Publikováno v:
Journal of the American College of Radiology.
Publikováno v:
JAMIA Open. 6
ObjectivesInter- and intra-observer variability is a concern for medical school admissions. Artificial intelligence (AI) may present an opportunity to apply a fair standard to all applicants systematically and yet maintain sensitivity to nuances that
Publikováno v:
Functional Neuroradiology ISBN: 9783031109089
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04da30383bc08238937447317c3a442b
https://doi.org/10.1007/978-3-031-10909-6_56
https://doi.org/10.1007/978-3-031-10909-6_56