Search Results - "Jin, Kwang Nam"

Accuracy and reproducibility of tumor size measurement using a deep-learning–based CDSS in resected lung cancer.

Authors: Kim, Eun Young Kim, Jun Seong Jin, Kwang Nam et al.

Source: PLoS ONE. 3/10/2026, Vol. 21 Issue 3, p1-9. 9p.

Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities.

Authors: Lee, Jong Eun Lee, Hyo-Jae Park, Gyeryeong et al.

Source: European Radiology. Apr2025, Vol. 35 Issue 4, p2265-2274. 10p.

Subjects: Receiver operating characteristic curves, COVID-19, Computed tomography, COVID-19 pandemic, Lung diseases

Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.

Authors: Oh, Se Jin Shin, Jae-ik Kim, Eun Na et al.

Source: Scientific Reports. 11/3/2025, Vol. 15 Issue 1, p1-14. 14p.

Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study.

Authors: Jin, Kwang Nam Kim, Eun Young Kim, Young Jae et al.

Source: European Radiology. May2022, Vol. 32 Issue 5, p3469-3479. 11p. 2 Color Photographs, 1 Diagram, 4 Charts, 2 Graphs.

AI Solution for Identifying Actionable Abnormalities on Chest Radiographs: A Retrospective Case-Control Study.

Alternate Title: 흉부 X선 검사에서 임상적 조치가 필요한 이상 소견 식별을 위한 상용 인공지능의 진단 성능: 후향적 환자-대조군 연구

Authors: Kim, Moon Young Yoo, Jin Young Jang, Yun Hee et al.

Source: Journal of the Korean Society of Radiology (2951-0805). May2026, Vol. 87 Issue 3, p560-573. 14p.

Mucus plug and lung cancer incidence in patients with COPD.

Authors: Kim, So Jeong Park, Heemoon Lee, Hyo Jin et al.

Source: Scientific Reports. 8/18/2025, Vol. 15 Issue 1, p1-10. 10p.

Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.

Authors: Lee, Ji Hyun Lee, Hyun Woo Lee, Hyo Jin et al.

Source: Scientific Reports. 4/10/2025, Vol. 15 Issue 1, p1-11. 11p.

Association between Image Characteristics on Chest CT and Severe Pleural Adhesion during Lung Cancer Surgery.

Authors: Jin, Kwang Nam Sung, Yong Won Oh, Se Jin et al.

Source: PLoS ONE. 5/12/2016, Vol. 11 Issue 5, p1-11. 11p.

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Authors: Choi, Yoon-Hee Kim, Dong Hyun Jeon, Eun-Tae et al.

Source: Scientific Reports. 7/23/2024, Vol. 14 Issue 1, p1-11. 11p.

Diagnostic performance of artificial intelligence model for pneumonia from chest radiography.

Authors: Kwon, TaeWoo Lee, Sang Pyo Kim, Dongmin et al.

Source: PLoS ONE. 4/15/2021, Vol. 16 Issue 4, p1-13. 13p.

Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort.

Authors: Kim, Eun Young Kim, Young Jae Choi, Won-Jun et al.

Source: PLoS ONE. 2/24/2022, Vol. 17 Issue 2, p1-12. 12p.

The Association of Brachial-Ankle Pulse Wave Velocity with Coronary Artery Disease Evaluated by Coronary Computed Tomography Angiography.

Authors: Kim, Hack-Lyoung Jin, Kwang Nam Seo, Jae-Bin et al.

Source: PLoS ONE. Apr2015, Vol. 10 Issue 4, p1-12. 12p.

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.

Authors: Kim, Eun Young Kim, Young Jae Choi, Won-Jun et al.

Source: PLoS ONE. 2/19/2021, Vol. 16 Issue 2, p1-12. 12p.

Machine learning-based prediction of in-ICU mortality in pneumonia patients.

Authors: Jeon, Eun-Tae Lee, Hyo Jin Park, Tae Yun et al.

Source: Scientific Reports. 7/17/2023, Vol. 13 Issue 1, p1-12. 12p.

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Authors: Ianni, Julianna D. Soans, Rajath E. Sankarapandian, Sivaramakrishnan et al.

Source: Scientific Reports. 2/21/2020, Vol. 10 Issue 1, p1-9. 9p.

ATTEMPTING TO INCREASE THE CLINICAL DIAGNOSTIC RATE OF ACUTE INTESTINAL ISCHEMIA USING MACHINE LEARNING ALGORITHMS.

Authors: Alexandru, Pîrvu Cătălin Nica, Cristian Dragoş, Mărgăritescu et al.

Source: Journal of Surgical Sciences. Apr-Jun2021, Vol. 8 Issue 2, p42-49. 8p.

Correction: Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.

Authors: Kim, Eun Young Kim, Young Jae Choi, Won-Jun et al.

Source: PLoS ONE. 4/28/2021, Vol. 16 Issue 4, p1-1. 1p.

Coronary artery assessment using self-navigated free-breathing radial whole-heart magnetic resonance angiography in patients with congenital heart disease.

Authors: Albrecht, Moritz H. Varga-Szemes, Akos Schoepf, U. Joseph et al.

Source: European Radiology. Mar2018, Vol. 28 Issue 3, p1267-1275. 9p. 2 Black and White Photographs, 3 Charts, 2 Graphs.

Subjects: Congenital heart disease, Magnetic resonance angiography, Coronary arteries, Radiology, Patients, Therapeutics

Initial experience of percutaneous transthoracic needle biopsy of lung nodules using C-arm cone-beam CT systems.

Authors: Jin KN Park CM Goo JM et al.

Source: European Radiology. Sep2010, Vol. 20 Issue 9, p2108-2115. 8p.