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AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia

Medical Image Analysis, ISSN: 1361-8415, Vol: 67, Page: 101860
2021
  • 110
    Citations
  • 0
    Usage
  • 300
    Captures
  • 2
    Mentions
  • 10
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    110
  • Captures
    300
  • Mentions
    2
    • News Mentions
      2
      • News
        2
  • Social Media
    10
    • Shares, Likes & Comments
      10
      • Facebook
        10

Most Recent News

Study of Thoracic CT in COVID-19: The STOIC Project

Université de Paris, APHP, Hôpital Cochin, Dept of Radiology, Paris, France Sorbonne Université, APHP, Hôpital Pitié Salpétrière, Dept of Radiology, Paris, France Université de Paris,

Article Description

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.

Bibliographic Details

Chassagnon, Guillaume; Vakalopoulou, Maria; Battistella, Enzo; Christodoulidis, Stergios; Hoang-Thi, Trieu-Nghi; Dangeard, Severine; Deutsch, Eric; Andre, Fabrice; Guillo, Enora; Halm, Nara; El Hajj, Stefany; Bompard, Florian; Neveu, Sophie; Hani, Chahinez; Saab, Ines; Campredon, Aliénor; Koulakian, Hasmik; Bennani, Souhail; Freche, Gael; Barat, Maxime; Lombard, Aurelien; Fournier, Laure; Monnier, Hippolyte; Grand, Téodor; Gregory, Jules; Nguyen, Yann; Khalil, Antoine; Mahdjoub, Elyas; Brillet, Pierre-Yves; Tran Ba, Stéphane; Bousson, Valérie; Mekki, Ahmed; Carlier, Robert-Yves; Revel, Marie-Pierre; Paragios, Nikos

Elsevier BV

Health Professions; Medicine; Computer Science

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