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.

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

Background: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and mortality. However, PE remains subject to misdiagnosis.