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Abstract
Background and Objectives: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: Out of 117 CT scans of 75 patients with COVID-19 admitted to our hospital between April and June 2020, we retrospectively analyzed 79 CT scans that had a definite time of onset and were performed prior to any medication intervention. Patients were grouped according to the presence or absence of increased oxygen demand after CT scan. Quantitative volume data of lung opacity were measured automatically using a deep learning-based image analysis system. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Results: All 79 CT scans were included (median age, 62 years (interquartile range, 46–77 years); 56 (70.9%) were male. The volume of opacity was significantly higher for the increased oxygen demand group than for the nonincreased oxygen demand group (585.3 vs. 132.8 mL, p < 0.001). The sensitivity, specificity, and AUC were 76.5%, 68.2%, and 0.737, respectively, in the prediction of increased oxygen demand. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management.
Highlights
The novel coronavirus disease 2019 (COVID-19) has rapidly spread worldwide since it was first reported in Wuhan, Hubei Province, China, in December 2019
This study aimed to quantitatively analyze the chest Computed tomography (CT) scans of patients with COVID-19 using a deep learning-based system to investigate the association between image data and the deterioration of the patient’s respiratory status
Oxygen demand increased after the CT scan in 45 cases (57.0%)
Summary
The novel coronavirus disease 2019 (COVID-19) has rapidly spread worldwide since it was first reported in Wuhan, Hubei Province, China, in December 2019. While many patients with COVID-19 are asymptomatic or present with mild symptoms and do not require hospitalization, the increasing number of patients with moderateto-severe respiratory conditions (requiring oxygenation or mechanical ventilation) depletes medical and human resources as well as disrupts daily practices [3]. This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management
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