Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods.

Journal: Acta radiologica (Stockholm, Sweden : 1987)

Published Date: Nov 17, 2020

Abstract

BACKGROUND: Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be underreported, or overlooked, especially in busy or under-staffed settings.

Authors

Haralabos Bougias

Department of Clinical Radiology, Ioannina University Hospital, Ioannina, Greece.
Eleni Georgiadou

Department of Medical Imaging, Metaxa Anticancer Hospital, Athens, Greece.
Christina Malamateniou

Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK.
Nikolaos Stogiannos

Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK.

Keywords

Algorithms Artificial Intelligence Cardiomegaly Cross-Sectional Studies Datasets as Topic Feasibility Studies Humans Logistic Models Machine Learning Predictive Value of Tests Radiography, Thoracic Reference Standards Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (33203215)

Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods.

Abstract

Authors

Keywords

External Resources

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