Roberta Fusco 1, Roberta Grassi 2,3, Vincenza Granata 4,*, Sergio Venanzio Setola 4 , Francesca Grassi 2,
Diletta Cozzi 5, Biagio Pecori 6, Francesco Izzo 7 and Antonella Petrillo 4
Abstract: Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19
using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning
and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several
electronic datasets were analyzed. The search covered the years from January 2019 to June 2021.
The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting
performance results in terms of accuracy or precision or area under Receiver Operating Characteristic
(ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based
on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision
of CXR in COVID-19 disease were 93.7% 10.0% of standard deviation (range 68.4–99.9%) and
95.7% 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean
value of the accuracy and specificity of CT in COVID-19 disease were 89.1% 7.3% of standard
deviation (range 78.0–99.9%) and 94.5 6.4% of standard deviation (range 86.0–100.0%), respectively.
No statistically significant difference in summarized accuracy mean value between CXR and CT
was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of
the selected papers is high but there was an important variability; however, less in CT studies
compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease
clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis,
and disease management.