Point of care image analysis for COVID-19

Yaron, D.; Keidar, D.; Goldstein, E.; Shachar, Y.; Oz Frank, A. B.; Schipper, N.; Shabshin, N.; Grubstein, A.; Suhami, D.; Bogot, N. R.; Weiss, C. S.; Sela, E.; Dror, A. A.; Vaturi, M.; Mento, F.; Torri, E.; Inchingolo, R.; Smargiassi, A.; Soldati, G.; Perrone, T.; Demi, L.; Galun, M.; Bagon, S.; Elyada, Y. M.; Eldar, Y. C.

Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to disinfect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading. © 2021 IEEE