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As the global population continues to face significant negative impact by theon-going COVID-19 pandemic, there has been an increasing usage of point-of-careultrasound (POCUS) imaging as a low-cost and effective imaging modality ofchoice in the COVID-19 clinical workflow. A major barrier with widespreadadoption of POCUS in the COVID-19 clinical workflow is the scarcity of expertclinicians that can interpret POCUS examinations, leading to considerableinterest in deep learning-driven clinical decision support systems to tacklethis challenge. A major challenge to building deep neural networks for COVID-19screening using POCUS is the heterogeneity in the types of probes used tocapture ultrasound images (e.g., convex vs. linear probes), which can lead tovery different visual appearances. In this study, we explore the impact ofleveraging extended linear-convex ultrasound augmentation learning on producingenhanced deep neural networks for COVID-19 assessment, where we conduct dataaugmentation on convex probe data alongside linear probe data that have beentransformed to better resemble convex probe data. Experimental results using anefficient deep columnar anti-aliased convolutional neural network designed viaa machined-driven design exploration strategy (which we name COVID-Net US-X)show that the proposed extended linear-convex ultrasound augmentation learningsignificantly increases performance, with a gain of 5.1% in test accuracy and13.6% in AUC.

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19  Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex  Ultrasound Augmentation Learning