Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging.

ABSTRACT:

Background

The automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices.

Methods

Our proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysi

SUBMITTER: Bridge J 

PROVIDER: S-EPMC10481527 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature