Invest Radiol 2021 Mar 5. Epub 2021 Mar 5.
From the Department of Radiology, Charité, Berlin Department of Radiology, Berlin Institute of Health (BIH), Charité, Berlin Institute of Oral and Maxillofacial Surgery, Charité, Berlin Department of Diagnostic and Interventional Radiology, Technical Universtity of Munich, School of Medicine, Munich, Germany.
Objectives: Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patients. Therefore, we aimed to develop and evaluate deep learning models for the identification of clinically relevant abnormalities in bedside CXRs, using reference standards established by computed tomography (CT) and multiple radiologists.
Materials And Methods: In this retrospective study, a dataset consisting of 18,361 bedside CXRs of patients treated at a level 1 medical center between January 2009 and March 2019 was used. Read More