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

Chassagnon, Guillaume; Vakalopoulou, Maria; Battistella, Enzo; Christodoulidis, Stergios; Hoang-Thi, Trieu Nghi; Dangeard, Séverine; Deutsch, Éric; Varga, Andrea; Guillo, Enora; Halm, Nara; Hajj, Stefany El; Bompard, Florian; Neveu, S.; Hani, Chahinez; Saab, Inès; Campredon, Aliénor; Koulakian, Hasmik; Bennani, Souhail; Freche, G.; Barat, Maxime; Lombard, Aurélien; Fournier, Laure; Monnier, Hippolyte; Grand, Téodor; Grégory, Jules; Nguyen, Yann; Khalil, Antoine; Mahdjoub, Elyas; Brillet, Pierre Yves; Ba, Stéphane Tran; Bousson, V.; Mekki, Ahmed; Carlier, Robert; Revel, Marie; Paragios, Nikos

doi:10.1016/j.media.2020.101860

Cited by 143 publications

(141 citation statements)

References 43 publications

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…Their model can identify 46 COVID‐19 cases that were previously missed by the RT‐PCR test 5 . Furthermore, quantitative information from CT images, such as the lung burden, the percentage of high opacity, and the lung severity score, can be used to monitor the disease progression and help us understand the course of COVID‐19 6,7 …”

Section: Introductionmentioning

confidence: 99%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

201

171

View full text Add to dashboard Cite

Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID‐19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data‐efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID‐19 cases, which contain current active research areas, for example, few‐shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state‐of‐the‐art network, we provide more than 40 pretrained baseline models, which not only serve as out‐of‐the‐box segmentation tools but also save computational time for researchers who are interested in COVID‐19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data‐efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID‐19 CT segmentation with limited data.

show abstract

Section: Introductionmentioning

confidence: 99%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

201

171

View full text Add to dashboard Cite

show abstract

“…Less extreme situations in emergency physicians and trauma surgeons result in PTSD [ 73 , 74 ]. Numerous predictive models of COVID-19 prognosis in various individuals based on AI-driven algorithms have been designed and published [ 75 , 76 , 77 , 78 , 79 , 80 ]. Their ability to distinguish between favorable outcomes and demise is significantly accurate.…”

Section: Resultsmentioning

confidence: 99%

What Can COVID-19 Teach Us about Using AI in Pandemics?

et al. 2020

View full text Add to dashboard Cite

The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.

show abstract

“… Chao et al. (2020) and Chassagnon et al. (2020) further extended the problem to patient outcome prediction, combining both imaging and non-imaging (clinical and biological) data.…”

Section: Related Workmentioning

confidence: 99%