Assessment of COVID-19 in lung ultrasound by combining anatomy and sonographic artifacts using deep learning

Abstract

When assessing severity of COVID19 from lung ultrasound (LUS) frames, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts, such as A-lines and B-lines are of importance. While ultrasound devices aim to provide an accurate visualization of the anatomy, the orientation of the sonographic artifacts differ between probe types. This difference poses a challenge in designing a unified deep artificial neural network capable of handling all probe types. In this work we improve upon Roy et al. (2020): We train a simple deep neural network to assess the severity of COVID-19 from LUS data. To address the challenge of handling both linear and convex probes in a unified manner we employed two strategies: First, we augment the input frames of convex probes with a “rectified” version in which A-lines and B-lines assume a horizontal/vertical aspect close to that achieved with linear probes. Second, we explicitly inform the network on the presence of important anatomical features and artifacts. We use a known Radon-based method for detecting the pleural line and B-lines and feed the detected lines as inputs to the network. Preliminary experiments yielded f1 = 68.7% compared to f1 = 65.1% reported by Roy et al.