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Cited by 169

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

Open access
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·2021·Medical Image Analysis
DOI 10.1016/j.media.2020.101860

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Abstract

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.

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Metadata

Authors
Chassagnon, G., Vakalopoulou, M., Battistella, E., Christodoulidis, S., Hoang-Thi, T.-N., Dangeard, S., Deutsch, E., Andre, F., Guillo, E., Halm, N., El Hajj, S., Bompard, F., Neveu, S., Hani, C., Saab, I., Campredon, A., Koulakian, H., Bennani, S., Freche, G., Barat, M., Lombard, A., Fournier, L., Monnier, H., Grand, T., Gregory, J., Nguyen, Y., Khalil, A., Mahdjoub, E., Brillet, P.-Y., Tran Ba, S., Bousson, V., Mekki, A., Carlier, R.-Y., Revel, M.-P., & Paragios, N.
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2021-01
Volume
67
Pages
101860
ISSN
1361-8415 (ISSN-L)
DOI
10.1016/j.media.2020.101860
OpenAlex
W3093455605
Cited by
169
References
18
FWCI
15.46
Type
journal article
Language
en
Open access
hybrid
Licence
Funders
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (188153)
  • Fondation pour la Recherche Médicale (DIC20161236437)
  • GE Healthcare
  • Horizon 2020 (880314)
  • Horizon 2020 Framework Programme

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CoChat. (2026). Citation check: AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. https://app.cochat.ai/papers/10.1016/j.media.2020.101860
The paper
Chassagnon, G., Vakalopoulou, M., Battistella, E., Christodoulidis, S., Hoang-Thi, T.-N., Dangeard, S., Deutsch, E., Andre, F., Guillo, E., Halm, N., El Hajj, S., Bompard, F., Neveu, S., Hani, C., Saab, I., Campredon, A., Koulakian, H., Bennani, S., Freche, G., . . . Paragios, N. (2021). AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Medical Image Analysis. https://doi.org/10.1016/j.media.2020.101860

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.