Zobrazeno 1 - 10
of 202
pro vyhledávání: '"Si Eun Lee"'
Autor:
Si Eun Lee, Hye Jung Kim, Hae Kyoung Jung, Jin Hyang Jung, Jae-Han Jeon, Jin Hee Lee, Hanpyo Hong, Eun Jung Lee, Daham Kim, Jin Young Kwak
Publikováno v:
Frontiers in Endocrinology, Vol 15 (2024)
Externí odkaz:
https://doaj.org/article/f8547faa4ac14a808df4b8395fa679e8
Publikováno v:
Cardiovascular Diabetology, Vol 23, Iss 1, Pp 1-11 (2024)
Abstract Background We assessed the efficacy and safety of enavogliflozin (0.3 mg), a newly developed SGLT-2 inhibitor, in patients with type 2 diabetes mellitus based on kidney function via pooled analysis of two 24-week, randomized, double-blind ph
Externí odkaz:
https://doaj.org/article/60b24246100e499594c4cfe5d70b4d87
Autor:
Si Eun Lee, Hye Jung Kim, Hae Kyoung Jung, Jin Hyang Jung, Jae-Han Jeon, Jin Hee Lee, Hanpyo Hong, Eun Jung Lee, Daham Kim, Jin Young Kwak
Publikováno v:
Frontiers in Endocrinology, Vol 15 (2024)
BackgroundData-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules.ObjectiveTo evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) f
Externí odkaz:
https://doaj.org/article/9349407c20a54569b1de68affb10a9e0
Publikováno v:
European Journal of Radiology Open, Vol 12, Iss , Pp 100545- (2024)
Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: Thi
Externí odkaz:
https://doaj.org/article/4f8a398d39d24bcbac5901ee16bee2ab
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at hig
Externí odkaz:
https://doaj.org/article/5c22c6936ea64281beb3d426b2e89ff7
Publikováno v:
European Journal of Radiology Open, Vol 11, Iss , Pp 100509- (2023)
Purpose: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. Methods: From January 2016 to December 2017, 6499 screening mammograms of 5
Externí odkaz:
https://doaj.org/article/334e8b4c51d9486ba9d80f1d7d6d6cfb
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-13 (2022)
Abstract Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative ex
Externí odkaz:
https://doaj.org/article/9646c140744e4558ad0719cadde3582d
Autor:
Si Eun Lee, Kyunghwa Han, Ji Hyun Youk, Jee Eun Lee, Ji-Young Hwang, Miribi Rho, Jiyoung Yoon, Eun-Kyung Kim, Jung Hyun Yoon
Publikováno v:
Ultrasonography, Vol 41, Iss 4, Pp 718-727 (2022)
Purpose This study evaluated how artificial intelligence-based computer-assisted diagnosis (AI-CAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workf
Externí odkaz:
https://doaj.org/article/d4358536cefe487ababbef747f0ab8ba
Publikováno v:
Ultrasonography, Vol 40, Iss 1, Pp 93-102 (2021)
Purpose The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. Methods A total of 496 patients (mean ag
Externí odkaz:
https://doaj.org/article/dcd3a0c72da84417a8ce598168f2bb96
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Abstract We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three dif
Externí odkaz:
https://doaj.org/article/075f8ef6b92e451985836936ea3be9f4