Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19

Abstract

Purpose As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. Materials and methods We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. Results The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. Conclusions The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

Quantitative Analysis for Lung Disease on Thin-Section CT

“…Lung involvement increases to consolidation up to two weeks after symptom onset [ 127 , 128 ]. Many CT studies have shown that a greater lesion extent on CT correlates with disease severity during COVID-19 pneumonia [ 129 ]. In initial studies, the extent was evaluated visually [ 130 ], but many dedicated software packages have been developed, including some using artificial intelligence [ 131 ].…”