Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study

• Main Authors: Hyun Woo Lee (Author), Hyun Jun Yang (Author), Hyungjin Kim (Author), Ue-Hwan Kim (Author), Dong Hyun Kim (Author), Soon Ho Yoon (Author), Soo-Youn Ham (Author), Bo Da Nam (Author), Kum Ju Chae (Author), Dabee Lee (Author), Jin Young Yoo (Author), So Hyeon Bak (Author), Jin Young Kim (Author), Jin Hwan Kim (Author), Ki Beom Kim (Author), Jung Im Jung (Author), Jae-Kwang Lim (Author), Jong Eun Lee (Author), Myung Jin Chung (Author), Young Kyung Lee (Author), Young Seon Kim (Author), Sang Min Lee (Author), Woocheol Kwon (Author), Chang Min Park (Author), Yun-Hyeon Kim (Author), Yeon Joo Jeong (Author), Kwang Nam Jin (Author), Jin Mo Goo (Author)
• Format: Book
• Published: JMIR Publications, 2023-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

BackgroundAn artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. ObjectiveWe aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients wit...