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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1978 2
1980 2
1982 1
1991 2
1993 5
1994 2
1995 3
1997 5
1998 3
1999 4
2000 4
2002 1
2004 1
2005 2
2006 2
2007 2
2008 5
2009 5
2010 2
2011 5
2012 4
2013 1
2014 3
2015 3
2018 2
2019 2
2020 3
2021 2
2022 2
2023 1
2024 0

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73 results

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Page 1
Induction of colonic regulatory T cells by indigenous Clostridium species.
Atarashi K, Tanoue T, Shima T, Imaoka A, Kuwahara T, Momose Y, Cheng G, Yamasaki S, Saito T, Ohba Y, Taniguchi T, Takeda K, Hori S, Ivanov II, Umesaki Y, Itoh K, Honda K. Atarashi K, et al. Science. 2011 Jan 21;331(6015):337-41. doi: 10.1126/science.1198469. Epub 2010 Dec 23. Science. 2011. PMID: 21205640 Free PMC article.
Induction of intestinal Th17 cells by segmented filamentous bacteria.
Ivanov II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U, Wei D, Goldfarb KC, Santee CA, Lynch SV, Tanoue T, Imaoka A, Itoh K, Takeda K, Umesaki Y, Honda K, Littman DR. Ivanov II, et al. Cell. 2009 Oct 30;139(3):485-98. doi: 10.1016/j.cell.2009.09.033. Cell. 2009. PMID: 19836068 Free PMC article.
Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation.
Yamada M, Shino R, Kondo H, Yamada S, Takamaru H, Sakamoto T, Bhandari P, Imaoka H, Kuchiba A, Shibata T, Saito Y, Hamamoto R. Yamada M, et al. J Gastroenterol. 2022 Nov;57(11):879-889. doi: 10.1007/s00535-022-01908-1. Epub 2022 Aug 16. J Gastroenterol. 2022. PMID: 35972582 Free PMC article.
BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classifi …
BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artific …
Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.
Yamada M, Saito Y, Imaoka H, Saiko M, Yamada S, Kondo H, Takamaru H, Sakamoto T, Sese J, Kuchiba A, Shibata T, Hamamoto R. Yamada M, et al. Sci Rep. 2019 Oct 8;9(1):14465. doi: 10.1038/s41598-019-50567-5. Sci Rep. 2019. PMID: 31594962 Free PMC article.
Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of col …
Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of mis …
73 results