Artificial intelligence-based CT metrics used in predicting clinical outcome of COVID-19 in young and middle-aged adults.

BACKGROUND

Currently, most researchers mainly analyzed coronavirus disease 2019 (COVID-19) pneumonia visually or qualitatively, probably somewhat time-consuming and not precise enough.

PURPOSE

This study aimed to excavate more information, such as differences in distribution, density, and severity of pneumonia lesions between males and females in a specific age group using artificial intelligence (AI)-based computed tomography (CT) metrics. Besides, these metrics were incorporated into a clinical regression model to predict the short-term outcome.

MATERIALS AND METHODS

The clinical, laboratory information and a series of HRCT images from 49 patients, aged from 20 to 50 years and confirmed with COVID-19, were collected. The volumes and percentages of infection (POIs) among bilateral lungs and each bronchopulmonary segment were extracted using uAI-Discover-NCP software (version R001). The POI in three HU ranges (i.e., <-300, -300-49, and ≥50 HU representing ground-glass opacity [GGO], mixed opacity, and consolidation) were also extracted. Hospital stay was predicted with several POI after adjusting days from illness onset to admission, leucocytes, lymphocytes, C-reactive protein, age, and gender using a multiple linear regression model. A total of 91 patients aged 20-50 from public database were selected.

RESULTS

Right lower lobes had the highest POI, followed by left lower lobes, right upper lobes, middle lobes, and left upper lobes. The distributions in lung lobes and segments were different between the sexes. Men had a higher total POI and GGO of the lungs, but less consolidation than women in initial CT (all p < 0.05). The total POI, percentage of consolidation on initial CT, and changed POI were positively correlated with hospital stay in the model. A total of 91 patients aged 20-50 years in the public database were selected, and AI segmentation was performed. The POI of the lower lobes was obviously higher than that in the upper lobes; the POI of each segment of the right upper lobe in the males was higher than that in the females, which was consistent with the result of the 49 patients previously.

CONCLUSION

Both men and women had characteristic distributions in lung lobes and bronchopulmonary segments. AI-based CT quantitative metrics can provide more precise information regarding lesion distribution and severity to predict clinical outcome.