Zobrazeno 1 - 10 of 51 pro vyhledávání: '"Sang Jin Sung"'
1
Akademický článek
The prediction of sagittal chin point relapse following two-jaw surgery using machine learning
Autor: Young Ho Kim, Inhwan Kim, Yoon-Ji Kim, Minji Kim, Jin-Hyoung Cho, Mihee Hong, Kyung-Hwa Kang, Sung-Hoon Lim, Su-Jung Kim, Namkug Kim, Jeong Won Shin, Sang-Jin Sung, Seung-Hak Baek, Hwa Sung Chae
Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract The study aimed to identify critical factors associated with the surgical stability of pogonion (Pog) by applying machine learning (ML) to predict relapse following two-jaw orthognathic surgery (2 J-OGJ). The sample set comprised 227 patient
Externí odkaz: https://doaj.org/article/e3be68d5eb004fd3a8897dc12c9a5006
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2
Akademický článek
Fully automated condyle segmentation using 3D convolutional neural networks
Autor: Nayansi Jha, Taehun Kim, Sungwon Ham, Seung-Hak Baek, Sang-Jin Sung, Yoon-Ji Kim, Namkug Kim
Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-8 (2022)
Abstract The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam compute
Externí odkaz: https://doaj.org/article/861f955519ef413cb7a042b1306ef438
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6
Vertical bony step between proximal and distal segments after mandibular setback is related with relapse: A cone-beam computed tomographic study
Autor: Myagmarsuren Batbold, Sung-Hoon Lim, Seo-Rin Jeong, Ji-Su Oh, Su-Jung Kim, Yoon-Ji Kim, Jin-Hyoung Cho, Kyung-Hwa Kang, Minji Kim, Mihee Hong, Sang-Jin Sung, Young Ho Kim, Jae Hyun Park, Seung-Hak Baek
Publikováno v: American Journal of Orthodontics and Dentofacial Orthopedics. 161:e524-e533
Vertical bony step (VBS) occurs between proximal and distal segments of the mandible during mandibular setback surgery with bilateral sagittal split ramus osteotomy. The purpose of this study was to investigate whether VBS is correlated with the rela
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6d9fa84e13de1c11ca29bc1d7cbb717
https://doi.org/10.1016/j.ajodo.2021.10.016
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7
Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi‐centres
Autor: Jaerong Kim, Inhwan Kim, Yoon‐Ji Kim, Minji Kim, Jin‐Hyoung Cho, Mihee Hong, Kyung‐Hwa Kang, Sung‐Hoon Lim, Su‐Jung Kim, Young Ho Kim, Namkug Kim, Sang‐Jin Sung, Seung‐Hak Baek
Publikováno v: Orthodontics & Craniofacial Research. 24:59-67
OBJECTIVE To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres. SETTINGS AND SAMPLE POPULATION A total
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3485c9ffdcc3a8cd918ab26d57d7083f
https://doi.org/10.1111/ocr.12493
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8
Projected lifetime cancer risk from cone-beam computed tomography for orthodontic treatment
Autor: Young-Jun Lee, Won Jin Lee, Sang-Jin Sung, Ju Young Lee, Nayansi Jha, Yoonji Kim
Publikováno v: Korean Journal of Orthodontics
Objective To estimate the projected cancer risk attributable to diagnostic cone-beam computed tomography (CBCT) performed under different exposure settings for orthodontic purposes in children and adults. Methods We collected a list of CBCT machines
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38207d27857d4c1a5a64dd2b0be3be3c
http://europepmc.org/articles/PMC8133899
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10
Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery
Autor: Mihee Hong, Inhwan Kim, Jin-Hyoung Cho, Kyung-Hwa Kang, Minji Kim, Su-Jung Kim, Yoon-Ji Kim, Sang-Jin Sung, Young Ho Kim, Sung-Hoon Lim, Namkug Kim, Seung-Hak Baek
Publikováno v: Korean journal of orthodontics. 52(4)
To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9382f81307c19817c4474d41b95bdd3
https://pubmed.ncbi.nlm.nih.gov/35719042
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