article Open AccessTop 1% cited
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. Zech(California Pacific Medical Center)Marcus A. BadgeleyManway LiuAnthony Costa(Neurological Surgery)J. Titano(Icahn School of Medicine at Mount Sinai)Eric K. Oermann✉(Neurological Surgery)
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
Citations
1,399
FWCI
96.41
field-weighted impact
References
42
Percentile
100%
vs. same field & year
Citations per year
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