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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
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 …
Contribution of DHA diols (19,20-DHDP) produced by cytochrome P450s and soluble epoxide hydrolase to the beneficial effects of DHA supplementation in the brains of rotenone-induced rat models of Parkinson's disease.
Oguro A, Ishihara Y, Siswanto FM, Yamazaki T, Ishida A, Imaishi H, Imaoka S. Oguro A, et al. Biochim Biophys Acta Mol Cell Biol Lipids. 2021 Feb;1866(2):158858. doi: 10.1016/j.bbalip.2020.158858. Epub 2020 Dec 3. Biochim Biophys Acta Mol Cell Biol Lipids. 2021. PMID: 33279658
A Potent Blood-Brain Barrier-Permeable Mutant IDH1 Inhibitor Suppresses the Growth of Glioblastoma with IDH1 Mutation in a Patient-Derived Orthotopic Xenograft Model.
Machida Y, Nakagawa M, Matsunaga H, Yamaguchi M, Ogawara Y, Shima Y, Yamagata K, Katsumoto T, Hattori A, Itoh M, Seki T, Nishiya Y, Nakamura K, Suzuki K, Imaoka T, Baba D, Suzuki M, Sampetrean O, Saya H, Ichimura K, Kitabayashi I. Machida Y, et al. Mol Cancer Ther. 2020 Feb;19(2):375-383. doi: 10.1158/1535-7163.MCT-18-1349. Epub 2019 Nov 14. Mol Cancer Ther. 2020. PMID: 31727689
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