Abstract

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.

Highlights

  • To investigate the value of artificial intelligence (AI) assisted quantification on initial chest computed tomography (CT) for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19)

  • The main finding of the current study confirmed the value of AI assisted lesion quantification for prediction of disease severity progression

  • The consolidation volume on initial chest CT was the strongest predictor among all CT-derived parameters and larger consolidation volume was associated with unfavorable clinical outcome

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Summary

Introduction

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome. Abbreviations AI Artificial intelligence COVID-19 Coronavirus disease 2019 CT Computed tomography GGO Ground glass opacity RT-PCR Reverse transcription polymerase chain reaction. The current study aims to investigate the value of AI assisted quantification for prediction of disease severity progression and clinical outcome, and compare it with conventional CT parameters evaluated by radiologist

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