Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort-Reference-Cited by-同舟云学术

Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort

Published:2022 Issue:10 Volume:23 Page:1009
ISSN:1229-6929
Container-title:Korean Journal of Radiology
language:en
Short-container-title:Korean J Radiol

Author:

Yoo Hyunsuk¹²^ORCID,Kim Eun Young³^ORCID,Kim Hyungjin²^ORCID,Choi Ye Ra⁴^ORCID,Kim Moon Young⁴^ORCID,Hwang Sung Ho⁵^ORCID,Kim Young Joong⁶^ORCID,Cho Young Jun⁶^ORCID,Jin Kwang Nam⁴^ORCID

Affiliation:

1. Lunit Inc, Seoul, Korea.

2. Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.

3. Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.

4. Department of Radiology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul, Korea.

5. Department of Radiology, Korea University Anam Hospital, Seoul, Korea.

6. Department of Radiology, Konyang University Hospital, Konyang University College of Medicine, Daejeon, Korea.

Funder

Korea Health Industry Development Institute

Gil Medical Center, Gachon University

Publisher

The Korean Society of Radiology

Subject

Radiology, Nuclear Medicine and imaging

Link

https://kjronline.org/pdf/10.3348/kjr.2022.0189

Reference29 articles.

1. Descriptive study of chest x-ray examination in mandatory annual health examinations at the workplace in Japan

2. Patients admitted to hospital with suspected pneumonia and normal chest radiographs: Epidemiology, microbiology, and outcomes

3. Abnormalities on Chest Radiograph Reported in Subjects in a Cancer Screening Trial

4. Prevalence of Abnormal Radiological Findings in Health Care Workers with Latent Tuberculosis Infection and Correlations with T Cell Immune Response

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