Point of Care Image Analysis for COVID-19

October 28, 2020 · Declared Dead · 🏛 IEEE International Conference on Acoustics, Speech, and Signal Processing

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Daniel Yaron, Daphna Keidar, Elisha Goldstein, Yair Shachar, Ayelet Blass, Oz Frank, Nir Schipper, Nogah Shabshin, Ahuva Grubstein, Dror Suhami, Naama R. Bogot, Eyal Sela, Amiel A. Dror, Mordehay Vaturi, Federico Mento, Elena Torri, Riccardo Inchingolo, Andrea Smargiassi, Gino Soldati, Tiziano Perrone, Libertario Demi, Meirav Galun, Shai Bagon, Yishai M. Elyada, Yonina C. Eldar arXiv ID 2011.01789 Category eess.IV: Image & Video Processing Cross-listed cs.CV Citations 12 Venue IEEE International Conference on Acoustics, Speech, and Signal Processing Last Checked 4 months ago

Abstract

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.