Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach

Huang, Lu; Han, Rui; Ai, Tao; Yu, Pengxin; Kang, Han; Tao, Qian; Xia, Liming

PURPOSE: To quantitatively evaluate lung burden changes in patients with COVID-19 using serial CT scan by an automated deep learning method. MATERIALS AND METHODS: Patients with COVID-19 who underwent chest CT between 1(st) January 2020 and 3(rd) February 2020 were retrospectively evaluated. Patients were divided into mild, moderate, severe, and critical types, according to their baseline clinical, laboratory, and CT findings. CT lung opacification percentage of the whole lung and five lobes were automatically quantified by a commercial deep learning software, and compared over follow-ups CT scans. Longitudinal changes of the CT quantitative parameter were also compared among the four clinical types. RESULTS: A total of 126 patients with COVID-19 (age 52 years ± 15 years, 53.2% males) were evaluated, including 6 mild, 94 moderate, 20 severe and 6 critical cases. CT-derived opacification percentage was significantly different among clinical groups at baseline, gradually progressing from mild to critical type (all P < 0.01). Overall, the whole-lung opacification percentage significantly increased between baseline CT and 1(st) follow-up CT (median [interquartile range]; 3.6% [0.5%,12.1%] vs 8.7% [2.7%,21.2%], P < 0.01). No significant progression of the opacification percentages was noted between the 1(st) follow-up and 2(nd) follow-up CT (8.7% [2.7%,21.2%] vs 6.0% [1.9%,24.3%], P=0.655). CONCLUSION: The quantification of lung opacification in COVID-19 measured on chest CT by a commercially available deep-learning-based tool was significantly different among different clinical severity groups. This approach could potentially eliminate the subjectivity in the initial assessment and follow up of pulmonary findings in COVID-19.