  Reverse transcription-polymerase chain reaction (RT-PCR) is currently the
gold standard in COVID-19 diagnosis. It can, however, take days to provide the
diagnosis, and false negative rate is relatively high. Imaging, in particular
chest computed tomography (CT), can assist with diagnosis and assessment of
this disease. Nevertheless, it is shown that standard dose CT scan gives
significant radiation burden to patients, especially those in need of multiple
scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT)
scan protocols that reduce the radiation exposure close to that of a single
X-Ray, while maintaining an acceptable resolution for diagnosis purposes. Since
thoracic radiology expertise may not be widely available during the pandemic,
we develop an Artificial Intelligence (AI)-based framework using a collected
dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can
provide human-level performance. The AI model uses a two stage capsule network
architecture and can rapidly classify COVID-19, community acquired pneumonia
(CAP), and normal cases, using LDCT/ULDCT scans. The AI model achieves COVID-19
sensitivity of 89.5% +\- 0.11, CAP sensitivity of 95% +\- 0.11, normal cases
sensitivity (specificity) of 85.7% +\- 0.16, and accuracy of 90% +\- 0.06. By
incorporating clinical data (demographic and symptoms), the performance further
improves to COVID-19 sensitivity of 94.3% +\- pm 0.05, CAP sensitivity of 96.7%
+\- 0.07, normal cases sensitivity (specificity) of 91% +\- 0.09 , and accuracy
of 94.1% +\- 0.03. The proposed AI model achieves human-level diagnosis based
on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the
proposed AI model has the potential to assist the radiologists to accurately
and promptly diagnose COVID-19 infection and help control the transmission
chain during the pandemic.
