Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs.

ABSTRACT: Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout perio

SUBMITTER: Ajmera P 

PROVIDER: S-EPMC9914339 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature