Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Hyunsuk, Yoo"'
Autor:
Hyunsuk Yoo
Publikováno v:
Korea Jouranl of Communication Studies. 30:147-167
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0da197af392ac6d58ffd442eb766f411
https://doi.org/10.23875/kca.30.3.6
https://doi.org/10.23875/kca.30.3.6
Publikováno v:
The Korea Association of Yeolin Education. 30:93-116
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08e6c528f5495d1e86d9319eb90d50c0
https://doi.org/10.18230/tjye.2022.30.1.93
https://doi.org/10.18230/tjye.2022.30.1.93
Autor:
Ju Hyun Park, Subba R. Digumarthy, Mannudeep K. Kalra, Hyunsuk Yoo, Hye Joung Eom, Chiara Arru, Sang Hyup Lee, Do Hoon Kim, Ruhani Doda Khera, Sean M. Siebert, Yuna Lee, Ramandeep Singh
Publikováno v:
European Radiology. 31:9664-9674
Assess if deep learning–based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs). This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from canc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46c650f498331bdcd3dc7024135483b8
https://doi.org/10.1007/s00330-021-08074-7
https://doi.org/10.1007/s00330-021-08074-7
Autor:
Han， Sangwook, Hyunsuk Yoo, Jinwook Kim, Kyoung-Shik Choi, Il-Oh Kang, Hyoungtae Kim, So-Jin Park
Publikováno v:
THE TRANSACTION OF THE KOREAN INSTITUTE OF ELECTRICAL ENGINEERS P. 69:207-213
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cab721ec98c5a250c183903e1efbe73
https://doi.org/10.5370/kieep.2020.69.4.207
https://doi.org/10.5370/kieep.2020.69.4.207
Publikováno v:
Transctions of the Korean Hydrogen and New Energy Society. 31:405-410
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97e561318ea0d2ff0abc45f947802a76
https://doi.org/10.7316/khnes.2020.31.5.405
https://doi.org/10.7316/khnes.2020.31.5.405
Autor:
Hyunsuk Yoo, Eun Young Kim, Hyungjin Kim, Ye Ra Choi, Moon Young Kim, Sung Ho Hwang, Young Joong Kim, Young Jun Cho, Kwang Nam Jin
Publikováno v:
Korean journal of radiology. 23(10)
This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment.This retrospective simulation study was conducted usi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d2ea819fc5bcae00481116c04b91a40
https://pubmed.ncbi.nlm.nih.gov/36175002
https://pubmed.ncbi.nlm.nih.gov/36175002
Autor:
Hyunsuk, Yoo, Sang Hyup, Lee, Chiara Daniela, Arru, Ruhani, Doda Khera, Ramandeep, Singh, Sean, Siebert, Dohoon, Kim, Yuna, Lee, Ju Hyun, Park, Hye Joung, Eom, Subba R, Digumarthy, Mannudeep K, Kalra
Publikováno v:
European radiology. 31(12)
Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from cancer-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::fb698e06ca0a3d513cb9d9992d894240
https://pubmed.ncbi.nlm.nih.gov/34089072
https://pubmed.ncbi.nlm.nih.gov/34089072
Publikováno v:
SSRN Electronic Journal.
Background: Interpretation of normal chest radiographs causes significant burden for the radiologists interpreting screening chest radiographs. This study aimed to simulate a workflow that uses artificial intelligence (AI) algorithm to triage normal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eaa03c3b3f1cb4c81e008dfdca08e757
https://doi.org/10.2139/ssrn.3742292
https://doi.org/10.2139/ssrn.3742292
Publikováno v:
AIDS and Behavior
We investigated whether mortality risk increases with the number of full-term pregnancies in HIV-infected women. Our study is based on data from the ACDIS cohort, collected in rural KwaZulu-Natal, South Africa. Mortality risk for different number of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b26a774f292a82dea8feb7ed324355b
https://doi.org/10.1007/s10461-018-2232-0
https://doi.org/10.1007/s10461-018-2232-0
Publikováno v:
JAMA Network Open
Key Points Question Does an artificial intelligence algorithm trained to detect pulmonary nodules improve lung cancer detection on chest radiographs? Findings In this diagnostic study of data from 5485 participants in the National Lung Screening Tria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d4984b21c64d7e9ec478db21845a8ec
https://doi.org/10.1001/jamanetworkopen.2020.17135
https://doi.org/10.1001/jamanetworkopen.2020.17135