Model E9

Model E9 is aimed at COVID-19 pneumonia patients with semantic CT features.

Model MU9 is a diagnostic tool that predicts risk of developing severe COVID-19 pneumonia using semantic features from chest CT.

Reference: Zhiyi Wang
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≥4e9/L     <4e9>

The model had an AUC of 0.956 [95%CI 0.935-0.977] on the training dataset and an AUC of 0.960 [95%CI 0.919-1.0, P < 0.001] on the validation dataset.

Outcome should be “Probability of severe illness in case of infection”.

The nomogram had a C-statistic of 0.967 (95% CI, 0.943–0.992) for predicting the probability in suspected COVID-19 pneumonia patients.

Suspected diagnostic criteria are:

1) Epidemiological history,

2) Fever and / or respiratory symptoms,

3) Suspected COVID-19 pneumonia imaging features,

4) Normal range or decrease of white blood cell (WBC) count or lymphocyte at beginning of disease. Patients who have epidemiological history + any two clinical symptoms or no epidemiological history + three clinical symptoms can be suspected of COVID-19 pneumonia