Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study.

ABSTRACT: The early detection of clinical deterioration and timely intervention for hospitalized patients can improve patient outcomes. The currently existing early warning systems rely on variables from structured data, such as vital signs and laboratory values, and do not incorporate other potentially predictive data modalities. Because respiratory failure is a common cause of deterioration, chest radiographs are often acquired in patients with clinical deterioration, which may be informative for predicting their risk of intensive care unit (ICU) transfer. This study aimed to compare and validate different computer vision models and data augmentation approaches with chest radiographs for predicting clinical deterioration. This retrospective observational study included adult patients hospitalized

SUBMITTER: Rahman M 

PROVIDER: S-EPMC12223691 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature