Selected article for: "lung ultrasound and lus detect"
Author: Yaron, Daniel; Keidar, Daphna; Goldstein, Elisha; Shachar, Yair; Blass, Ayelet; Frank, Oz; Schipper, Nir; Shabshin, Nogah; Grubstein, Ahuva; Suhami, Dror; Bogot, Naama R.; Sela, Eyal; Dror, Amiel A.; Vaturi, Mordehay; Mento, Federico; Torri, Elena; Inchingolo, Riccardo; Smargiassi, Andrea; Soldati, Gino; Perrone, Tiziano; Demi, Libertario; Galun, Meirav; Bagon, Shai; Elyada, Yishai M.; Eldar, Yonina C.
Title: Point of Care Image Analysis for COVID-19
  • Cord-id: h51sapx3
  • Document date: 2020_10_28
    • ID: h51sapx3
    Snippet: 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
    Document: 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.

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