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

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. For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals.

Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs.

Background: Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition.