  The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of
life globally, and a critical factor in mitigating its effects is screening
individuals for infections, thereby allowing for both proper treatment for
those individuals as well as action to be taken to prevent further spread of
the virus. Point-of-care ultrasound (POCUS) imaging has been proposed as a
screening tool as it is a much cheaper and easier to apply imaging modality
than others that are traditionally used for pulmonary examinations, namely
chest x-ray and computed tomography. Given the scarcity of expert radiologists
for interpreting POCUS examinations in many highly affected regions around the
world, low-cost deep learning-driven clinical decision support solutions can
have a large impact during the on-going pandemic. Motivated by this, we
introduce COVID-Net US, a highly efficient, self-attention deep convolutional
neural network design tailored for COVID-19 screening from lung POCUS images.
Experimental results show that the proposed COVID-Net US can achieve an AUC of
over 0.98 while achieving 353X lower architectural complexity, 62X lower
computational complexity, and 14.3X faster inference times on a Raspberry Pi.
Clinical validation was also conducted, where select cases were reviewed and
reported on by a practicing clinician (20 years of clinical practice)
specializing in intensive care (ICU) and 15 years of expertise in POCUS
interpretation. To advocate affordable healthcare and artificial intelligence
for resource-constrained environments, we have made COVID-Net US open source
and publicly available as part of the COVID-Net open source initiative.
