AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
Medical Image Analysis, ISSN: 1361-8415, Vol: 67, Page: 101860
2021
- 110Citations
- 300Captures
- 2Mentions
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations110
- Citation Indexes110
- 110
- CrossRef81
- Captures300
- Readers300
- 300
- Mentions2
- News Mentions2
- News2
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
http://www.sciencedirect.com/science/article/pii/S1361841520302243; http://dx.doi.org/10.1016/j.media.2020.101860; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85095434755&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/33171345; https://linkinghub.elsevier.com/retrieve/pii/S1361841520302243; https://dx.doi.org/10.1016/j.media.2020.101860
Elsevier BV
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