Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs

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. To develop a deep learning-based algorithm that can classify normal and abnormal results...

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Bibliographic Details
Published in:JAMA network open 2019-03, Vol.2 (3), p.e191095-e191095
Main Authors: Hwang, Eui Jin, Park, Sunggyun, Jin, Kwang-Nam, Kim, Jung Im, Choi, So Young, Lee, Jong Hyuk, Goo, Jin Mo, Aum, Jaehong, Yim, Jae-Joon, Cohen, Julien G, Ferretti, Gilbert R, Park, Chang Min
Format: Article
Language:English
Subjects:
Adult
Aged
Algorithms
Automation
Classification
Deep Learning
Female
Humans
Imaging
Localization
Male
Middle Aged
Online Only
Original Investigation
Radiographic Image Interpretation, Computer-Assisted - methods
Radiography, Thoracic - methods
Reproducibility of Results
Thoracic Diseases - diagnostic imaging
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Summary: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. 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. 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 (21 556 men and 26 361 women; mean [SD] age, 51 [16] years) and 35 613 chest radiographs with abnormal findings from 14 102 individuals (8373 men and 5729 women; mean [SD] age, 62 [15] years) were used to develop the algorithm. A total of 486 chest radiographs with normal results and 529 with abnormal results (1 from each participant; 628 men and 387 women; mean [SD] age, 53 [18] years) from 5 institutions were used for external validation. Fifteen physicians, including nonradiology physicians, board-certified radiologists, and thoracic radiologists, participated in observer performance testing. Data were analyzed in August 2018. Deep learning-based algorithm. Image-wise classification performances measured by area under the receiver operating characteristic curve; lesion-wise localization performances measured by area under the alternative free-response receiver operating characteristic curve. The algorithm demonstrated a median (range) area under the curve of 0.979 (0.973-1.000) for image-wise classification and 0.972 (0.923-0.985) for lesion-wise localization; the algorithm demonstrated significantly higher performance than all 3 physician groups in both image-wise classification (0.983 vs 0.814-0.932; all P 
ISSN:2574-3805
2574-3805
DOI:10.1001/jamanetworkopen.2019.1095