AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

ABSTRACT:

Objectives

Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.

Methods

We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and G

SUBMITTER: Meng F 

PROVIDER: S-EPMC9755771 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature