Title: Ultrasound : Worth A Consideration For Automatic Detection Of COVID-19

Abstract:

With the rapid development of COVID-19 into a global pandemic, there is an urgent

need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound (POCUS) imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe.

Our contribution is threefold. First, we gather a lung ultrasound dataset consisting

of more than 2000 images sampled from 131 videos for COVID-19, bacterial pneumonia and healthy controls. While this dataset was assembled from various online sources and is by no means exhaustive, it was processed specifically to feed deep learning models and is intended to serve as a starting point for an open-access initiative.Second, we train a deep convolutional neural network (POCOVID-Net) which achieves an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation.

Third, we provide an open-access web service (POCOVIDScreen) that is available

at: https://pocovidscreen.org. The website deploys the predictive model, allowing

to perform predictions on ultrasound lung images. In addition, it grants medical sta the

option to (bulk) upload their own screenings in order to contribute to the growing public

database of pathological lung ultrasound images.