Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm.

Journal: Clinical radiology

PMID: 33637309

Abstract

AIM: To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as a rule-out test for fully automated diagnosis in frontal adult chest radiographs (CXR) in an active clinical pathway.

Authors

T Dyer

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK. Electronic address: tomd@behold.ai.
L Dillard

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK.
M Harrison

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK.
T Naunton Morgan

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK.
R Tappouni

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK; Department of Radiology, Wake Forest Baptist Health, North Carolina, USA.
Q Malik

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK; Department of Radiology, Basildon and Thurrock NHS Trust, Essex, UK.
S Rasalingham

Behold.ai Technologies Limited, WeWork South Bank, 22 Upper Ground, London, SE1 9PD, UK.

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Radiography, Thoracic Reference Values Reproducibility of Results Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (33637309)

Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm.

Abstract

Authors

Keywords

External Resources

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