  Purpose: to optimize a pipeline of clinical data gathering and CT images
processing implemented during the COVID-19 pandemic crisis and to develop
artificial intelligence model for different of viral pneumonia. Methods: 1028
chest CT image of patients with positive swab were segmented automatically for
lung extraction. A Gaussian model developed in Python language was applied to
calculate quantitative metrics (QM) describing well-aerated and ill portions of
the lungs from the histogram distribution of lung CT numbers in both lungs of
each image and in four geometrical subdivision. Furthermore, radiomic features
(RF) of first and second order were extracted from bilateral lungs using
PyRadiomic tools. QM and RF were used to develop 4 different Multi-Layer
Perceptron (MLP) classifier to discriminate images of patients with COVID
(n=646) and non-COVID (n=382) viral pneumonia. Results: The Gaussian model
applied to lung CT histogram correctly described healthy parenchyma 94% of the
patients. The resulting accuracy of the models for COVID diagnosis were in the
range 0.76-0.87, as the integral of the receiver operating curve. The best
diagnostic performances were associated to the model based on RF of first and
second order, with 21 relevant features after LASSO regression and an accuracy
of 0.81$\pm$0.02 after 4-fold cross validation Conclusions: Despite these
results were obtained with CT images from a single center, a platform for
extracting useful quantitative metrics from CT images was developed and
optimized. Four artificial intelligence-based models for classifying patients
with COVID and non-COVID viral pneumonia were developed and compared showing
overall good diagnostic performances
