Validation study of machine-learning chest radiograph software in primary and emergency medicine.

Journal: Clinical radiology

Published Date: Sep 25, 2022

Abstract

AIM: To evaluate the performance of a machine learning based algorithm tool for chest radiographs (CXRs), applied to a consecutive cohort of historical clinical cases, in comparison to expert chest radiologists.

Authors

E J R van Beek

Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK; Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, UK. Electronic address: edwin-vanbeek@ed.ac.uk.
J S Ahn

Lunit, Seoul, South Korea.
M J Kim

St.Mary's Hospital, Cardiology, Incheon, Korea Republic of.
J T Murchison

Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, UK.

Keywords

Artificial Intelligence Deep Learning Emergency Medicine Humans Machine Learning Pleural Effusion Radiography, Thoracic Retrospective Studies Software

External Resources

View on PubMed Access via DOI PubMed (36171164)

Validation study of machine-learning chest radiograph software in primary and emergency medicine.

Abstract

Authors

Keywords

External Resources

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