  We describe a novel, two-stage computer assistance system for lung anomaly
detection using ultrasound imaging in the intensive care setting to improve
operator performance and patient stratification during coronavirus pandemics.
The proposed system consists of two deep-learning-based models: a quality
assessment module that automates predictions of image quality, and a diagnosis
assistance module that determines the likelihood-oh-anomaly in ultrasound
images of sufficient quality. Our two-stage strategy uses a novelty detection
algorithm to address the lack of control cases available for training the
quality assessment classifier. The diagnosis assistance module can then be
trained with data that are deemed of sufficient quality, guaranteed by the
closed-loop feedback mechanism from the quality assessment module. Using more
than 25000 ultrasound images from 37 COVID-19-positive patients scanned at two
hospitals, plus 12 control cases, this study demonstrates the feasibility of
using the proposed machine learning approach. We report an accuracy of 86% when
classifying between sufficient and insufficient quality images by the quality
assessment module. For data of sufficient quality - as determined by the
quality assessment module - the mean classification accuracy, sensitivity, and
specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97,
respectively, across five holdout test data sets unseen during the training of
any networks within the proposed system. Overall, the integration of the two
modules yields accurate, fast, and practical acquisition guidance and
diagnostic assistance for patients with suspected respiratory conditions at
point-of-care.
