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 SO2 (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.