Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study.

ABSTRACT:

Background

Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention.

Objective

The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data.

Methods

We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free).

Results

Using the mixed ACN

SUBMITTER: Ho TT 

PROVIDER: S-EPMC7850779 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature