In Journal of medical Internet research ; h5-index 88.0
BACKGROUND : An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.
OBJECTIVE : We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.
METHODS : This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration.
RESULTS : The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859).
CONCLUSIONS : The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.
Lee Hyun Woo, Yang Hyun Jun, Kim Hyungjin, Kim Ue-Hwan, Kim Dong Hyun, Yoon Soon Ho, Ham Soo-Youn, Nam Bo Da, Chae Kum Ju, Lee Dabee, Yoo Jin Young, Bak So Hyeon, Kim Jin Young, Kim Jin Hwan, Kim Ki Beom, Jung Jung Im, Lim Jae-Kwang, Lee Jong Eun, Chung Myung Jin, Lee Young Kyung, Kim Young Seon, Lee Sang Min, Kwon Woocheol, Park Chang Min, Kim Yun-Hyeon, Jeong Yeon Joo, Jin Kwang Nam, Goo Jin Mo
2023-Feb-16
AI model, COVID-19, artificial intelligence, clinical outcome, deep learning, machine learning, medical imaging, prediction model, prognosis, radiography, thoracic
