Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters

RATIONALE AND OBJECTIVES: To demonstrate the first experience of a deep learning-based algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of COVID-19 patients and to correlate quantitative results with clinical and laboratory parameters. MATERIALS AND METHODS: We retrospectively included 60 consecutive patients (mean age, 61 ± 12 years; 18 females) with proven COVID-19 infection undergoing chest CT between March and May 2020. Clinical and laboratory data (within 24 h before/after chest CT) were recorded. Prototype software using a deep learning algorithm was applied for automatic segmentation and quantification of lung opacities. Percentage of opacity (PO, ground-glass and consolidations) and percentage of high opacity (PHO, consolidations), were defined as 100 times the volume of segmented abnormalities divided by the volume of the lung mask. RESULTS: Automatic CT analysis of the lung was feasible in all patients (n = 60). The median time to accomplish automatic evaluation was 120 s (IQR: 118–128 s). In four cases (7 %), manual corrections were necessary. Patients with need for mechanical ventilation had a significantly higher PO (median 44 %, IQR: 23–58 % versus 13 %, IQR: 10–24 %; p = 0.001) and PHO (median: 11 %, IQR: 6–21 % versus 3%, IQR: 2–7 %, p = 0.002) compared to those without. The PO and PHO moderately correlated with c-reactive protein (r = 0.49−0.60, both p < 0.001) and leucocyte count (r = 0.30−0.40, both p = 0.05). PO had a negative correlation with SO(2) (r=−0.50, p = 0.001). CONCLUSION: Preliminary experience indicates the feasibility of a rapid, automatic quantification tool of lung parenchymal abnormalities in COVID-19 patients using deep learning, with results correlating with laboratory and clinical parameters.