Seung-Jin Yoo
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Explore the profile of Seung-Jin Yoo including associated specialties, affiliations and a list of published articles.
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37
Citations
172
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Recent Articles
1.
Hwang E, Hong H, Ko S, Yoo S, Kim H, Kim D, et al.
Radiol Artif Intell
. 2025 Jan;
7(1):e240215.
PMID: 39812585
Quantifying pleural effusion change at chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based...
2.
Yoo S, Kim B, Kim H, Choi S
J Clin Med
. 2025 Jan;
14(1.
PMID: 39797301
To evaluate the clinical and radiologic outcomes of arthroscopic augmented partial repair (APR) with acellular dermal matrix versus arthroscopic superior capsular reconstruction (SCR) in massive rotator cuff tears. The study...
3.
Song Y, Kim H, Cho S, Kim H, Lim C, Nam E, et al.
Arthritis Res Ther
. 2024 Jul;
26(1):137.
PMID: 39030584
Objectives: To determine the risk factors for mortality in Korean patients with rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) in comparison to patients with RA but without ILD (RA-nonILD). Methods:...
4.
Seo B, Choi J, Chang B, Kim B, Park T, Lee H, et al.
Sci Rep
. 2024 May;
14(1):10347.
PMID: 38710892
The aim of the study was to investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with limited-stage small-cell lung cancer (LS-SCLC) undergoing definite chemo-radiotherapy...
5.
Yoo S, Park Y, Choi H, Kim D, Goo J, Yoon S
PLoS One
. 2024 Feb;
19(2):e0297390.
PMID: 38386632
Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). Methods: The institutional review board...
6.
Yoo S, Kim H, Witanto J, Inui S, Yoon J, Lee K, et al.
Eur J Radiol
. 2023 May;
164:110858.
PMID: 37209462
Purpose: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. Materials And Methods: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in...
7.
Kim H, Cho S, Song Y, Kang J, Jeong S, Kim H, et al.
Arthritis Res Ther
. 2023 Mar;
25(1):43.
PMID: 36932433
Background: To introduce a prospective cohort for rheumatoid arthritis (RA) patients with interstitial lung disease (ILD) and to identify their clinical features in comparison with RA patients without ILD. Methods:...
8.
Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network
Lee W, Shin K, Lee J, Yoo S, Yoon M, Choi Y, et al.
J Korean Soc Radiol
. 2022 Dec;
83(6):1298-1311.
PMID: 36545424
Purpose: To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials And Methods: Using...
9.
Kim D, Yoo S, Hong J, Kwak N, Yim J, Yoon S
Eur J Radiol Open
. 2022 Nov;
9:100452.
PMID: 36420119
Objective: To prospectively evaluate the image quality and diagnostic performance of a compact flat-panel detector (FD) scanner for thoracic diseases compared to a clinical CT scanner. Materials And Methods: The...
10.
Kim H, Jin K, Yoo S, Lee C, Lee S, Hong H, et al.
Radiology
. 2022 Oct;
306(3):e220292.
PMID: 36283113
Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based,...