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Abstract:

Objective To study the evaluation and prediction value of CT visual quantitative assessment combined with artificial intelligence（AI）for the severity of Coronavirus Disease 2019（COVID ⁃ 19）. Method The clinical and imaging data of 86 patients with COVID⁃19，including severe/critical cases（n = 31）and ordinary cases（n = 55），were studied retrospectively. The characteristic CT parameter variation of thetwo groups was analyzed and compared. The number of involved pulmonary segments was analyzed using the receiver operating characteristic curve（ROCcurve）. CTscores and two visual quantitative assessment methods were used to evaluate and predictthe ability ofordinary and severe⁃critical COVID⁃19. Moreover，the artificial intelligence software was utilized to predict the severity of COVID ⁃19 and calculatethe specificity and sensitivity of severe ⁃critical COVID ⁃ 19. Results The median CT score and the median number of involved pulmonary segments in thesevere ⁃critical groupwere signifi⁃ cantly higher than those in the ordinary group（P < 0.001）. The difference in ground⁃glass opacity（GGO），consoli⁃ dation，subpleural line，and“crazy⁃paving”sign between the two groupswas not statistically significant. The results of ROC curve analysis showed that the area under the curve（AUC）is 0.879 when the CT score was used for prediction of severe⁃critical COVID⁃19，and the sensitivity was 71.0%，and the specificity is 90.9% at the cutoff value of 8.5. The AUC was 0.883 when the number of involved pulmonary segments was used to predict the severe⁃ critical COVID ⁃ 19. When the cut ⁃ off value was 10.5，the sensitivity was 87.1% and the specificity was 81.8%. When AI was used for prediction of severe⁃critical COVID⁃19，the sensitivity was 77.4% and specificity was 92.7%. Conclusions CT visual quantitative assessment combined with artificial intelligence could be used to assess and predict theseverity of COVID⁃19，

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