Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.

Journal: PloS one

Published Date: Jan 1, 2021

Abstract

BACKGROUND: The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of this endpoint.

Authors

Yiyun Chen

School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China; State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China. Electronic address: chenyy@whu.edu.cn.
Craig S Roberts

Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Wanmei Ou

Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Tanaz Petigara

Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Gregory V Goldmacher

Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Nicholas Fancourt

Menzies School of Health Research, Charles Darwin University, Darwin, Australia.
Maria Deloria Knoll

Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.

Keywords

Datasets as Topic Deep Learning Female Humans Image Interpretation, Computer-Assisted Infant Male Models, Statistical Observer Variation Pneumonia Radiography, Thoracic Reproducibility of Results ROC Curve World Health Organization

External Resources

View on PubMed Access via DOI PubMed (34153076)

Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.

Abstract

Authors

Keywords

External Resources

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