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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

PLoS Medicine · 2018 · Vol. 15(11) · pp. e1002683–e1002683
John R. ZechMarcus A. BadgeleyManway LiuAnthony CostaJ. TitanoEric K. Oermann

Abstract

Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

COVID-19 diagnosis using AIMachine Learning in HealthcareAI in cancer detectionPneumoniaCross-sectional studyMedicineGeneralizationRadiographyArtificial intelligenceInternal medicineRadiologyPathologyComputer science

MeSH terms

Deep LearningAdultAgedCross-Sectional StudiesDiagnosis, Computer-AssistedFemaleHumansMaleMiddle AgedPneumoniaPredictive Value of TestsRadiographic Image Interpretation, Computer-AssistedRadiology Information SystemsRetrospective StudiesRadiography, Thoracic

Funding

  • Icahn School of Medicine at Mount Sinai
  • Mount Sinai Health System
  • National Institutes of Health
  • U.S. National Library of Medicine
  • NIH Clinical Center
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