About: Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Creator	Wang, Qian Wang, Jun Shi, Feng Shi, Jun Shi, F Wang, J Shi, J Wang, ) Shen, D Shen, Dinggang Tang, Zhenyu He, Kelei Shi, Yinghuan Wu, Ziyan
Source	ArXiv; Medline
abstract	(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1109/rbme.2020.2987975
bibo:pmid	32305937
has license	arxiv
sha1sum (hex)	4e95ce827049a350819c5c87caf992f27f9ff792
schema:url	https://doi.org/10.1109/rbme.2020.2987975
resource representing a document's title	Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
has PubMed identifier	32305937
schema:publication	IEEE reviews in biomedical engineering
resource representing a document's body	covid:4e95ce827049a350819c5c87caf992f27f9ff792#body_text
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