  Early detection of COVID-19 is key in containing the pandemic. Disease
detection and evaluation based on imaging is fast and cheap and therefore plays
an important role in COVID-19 handling. COVID-19 is easier to detect in chest
CT, however, it is expensive, non-portable, and difficult to disinfect, making
it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray
(CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in
these modalities are not always very clear. Here we train deep neural networks
to significantly enhance the capability to detect, grade and monitor COVID-19
patients using CXRs and LUS. Collaborating with several hospitals in Israel we
collect a large dataset of CXRs and use this dataset to train a neural network
obtaining above 90% detection rate for COVID-19. In addition, in collaboration
with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we
obtained POC ultrasound data with annotations of the severity of disease and
trained a deep network for automatic severity grading.
