作者
Eui Jin Hwang, Sunggyun Park, Kwang-Nam Jin, Jung Im Kim, So Young Choi, Jong Hyuk Lee, Jin Mo Goo, Jaehong Aum, Jae-Joon Yim, Julien G Cohen, Gilbert R Ferretti, Chang Min Park
发表日期
2019/3/1
期刊
JAMA network open
卷号
2
期号
3
页码范围
e191095-e191095
出版商
American Medical Association
简介
Importance
Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow.
Objectives
To develop a deep learning–based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases including pulmonary malignant neoplasm, active tuberculosis, pneumonia, and pneumothorax and to validate the algorithm’s performance using independent data sets.
Design, Setting, and Participants
This diagnostic study developed a deep learning–based algorithm using single-center data collected between November 1, 2016, and January 31, 2017. The algorithm was externally validated with multicenter data collected between May 1 and July 31, 2018. A total of 54 221 chest radiographs with normal findings from 47 917 individuals (21 …
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EJ Hwang, S Park, KN Jin, J Im Kim, SY Choi, JH Lee… - JAMA network open, 2019