TY - JOUR
T1 - Point of care image analysis for COVID-19
AU - Yaron, Daniel
AU - Keidar, Daphna
AU - Goldstein, Elisha
AU - Shachar, Yair
AU - Oz Frank, Ayelet Blass
AU - Schipper, Nir
AU - Shabshin, Nogah
AU - Grubstein, Ahuva
AU - Suhami, Dror
AU - Bogot, Naama R.
AU - Weiss, Chedva S.
AU - Sela, Eyal
AU - Dror, Amiel A.
AU - Vaturi, Mordehay
AU - Mento, Federico
AU - Torri, Elena
AU - Inchingolo, Riccardo
AU - Smargiassi, Andrea
AU - Soldati, Gino
AU - Perrone, Tiziano
AU - Demi, Libertario
AU - Galun, Meirav
AU - Bagon, Shai
AU - Elyada, Yishai M.
AU - Eldar, Yonina C.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85115069762
U2 - 10.1109/ICASSP39728.2021.9413687
DO - 10.1109/ICASSP39728.2021.9413687
M3 - Conference article
AN - SCOPUS:85115069762
SN - 1520-6149
VL - 2021-June
SP - 8153
EP - 8157
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
Y2 - 6 June 2021 through 11 June 2021
ER -