Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19.

ABSTRACT:

Purpose

As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19.

Materials and methods

We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by int

SUBMITTER: Kawata N 

PROVIDER: S-EPMC10687147 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature