Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ryan C Bahar"'
Autor:
Ryan C. Bahar, Aidan W. O’Shea, Eric S. Li, Madisen A. Swallow, August A. Allocco, Judy M. Spak, Janet P. Hafler
Publikováno v:
Medical Education Online, Vol 27, Iss 1 (2022)
In the past forty years, clinician-educators have become indispensable to academic medicine. Numerous clinician-educator-training programs exist within graduate medical education (GME) as clinician-educator tracks (CETs). However, there is a call for
Externí odkaz:
https://doaj.org/article/23937236f2d24342a75c8c58b9dbef15
Autor:
Ryan C. Bahar, Sara Merkaj, Gabriel I. Cassinelli Petersen, Niklas Tillmanns, Harry Subramanian, Waverly Rose Brim, Tal Zeevi, Lawrence Staib, Eve Kazarian, MingDe Lin, Khaled Bousabarah, Anita J. Huttner, Andrej Pala, Seyedmehdi Payabvash, Jana Ivanidze, Jin Cui, Ajay Malhotra, Mariam S. Aboian
Publikováno v:
Frontiers in Oncology, Vol 12 (2022)
ObjectivesTo systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.MethodsThis study followed the Preferred Reporting Items for Sy
Externí odkaz:
https://doaj.org/article/0185748a65b042c88401ca7ad068272f
Autor:
Niklas Tillmanns, Avery E Lum, Gabriel Cassinelli, Sara Merkaj, Tej Verma, Tal Zeevi, Lawrence Staib, Harry Subramanian, Ryan C Bahar, Waverly Brim, Jan Lost, Leon Jekel, Alexandria Brackett, Sam Payabvash, Ichiro Ikuta, MingDe Lin, Khaled Bousabarah, Michele H Johnson, Jin Cui, Ajay Malhotra, Antonio Omuro, Bernd Turowski, Mariam S Aboian
Publikováno v:
Neuro-oncology advances. 4(1)
BackgroundWhile there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::283405c1bfe570b8437eee76be9bee91
https://pubmed.ncbi.nlm.nih.gov/36071926
https://pubmed.ncbi.nlm.nih.gov/36071926
Autor:
Sara Merkaj, Ryan C. Bahar, Tal Zeevi, MingDe Lin, Ichiro Ikuta, Khaled Bousabarah, Gabriel I. Cassinelli Petersen, Lawrence Staib, Seyedmehdi Payabvash, John T. Mongan, Soonmee Cha, Mariam S. Aboian
Publikováno v:
Cancers, vol 14, iss 11
Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e22fef363cb3a6951195f0564382d6c
https://doi.org/10.3390/cancers14112623
https://doi.org/10.3390/cancers14112623