A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

ABSTRACT:

Objectives

To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

Methods

A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.

SUBMITTER: Ni Q 

PROVIDER: S-EPMC7331494 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature