Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

ABSTRACT: The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia

SUBMITTER: Ortiz A 

PROVIDER: S-EPMC8810911 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature