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

ABSTRACT:

Background

There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task.

Methods and findings

A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana Universit

SUBMITTER: Zech JR 

PROVIDER: S-EPMC6219764 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature