About: Any unique image biomarkers associated with COVID-19?

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Any unique image biomarkers associated with COVID-19?
Creator	Guo, Youmin Wilson, David Fuhrman, Carl Sciurba, Frank Leader, Joseph Bandos, Andriy Du, Pang Field, Jessica Ke, Shi Pu, Jiantao Shi, Junli Yang, Bohan Yu, Juezhao Chenwang, &
Source	Medline; PMC
abstract	OBJECTIVE: To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. METHODS: We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary. RESULTS: One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56–0.85). This model allowed for the identification of 8–50% of CAP patients with only 2% of COVID-19 patients. CONCLUSIONS: Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. KEY POINTS: • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.
has issue date	2020-05-28(xsd:dateTime)
bibo:doi	10.1007/s00330-020-06956-w
bibo:pmid	32462445
has license	no-cc
sha1sum (hex)	65954dcce2c297b5a195b24e2f6cf6e13087c3ed
schema:url	https://doi.org/10.1007/s00330-020-06956-w
resource representing a document's title	Any unique image biomarkers associated with COVID-19?
has PubMed Central identifier	PMC7253230
has PubMed identifier	32462445
schema:publication	Eur Radiol
resource representing a document's body	covid:65954dcce2c297b5a195b24e2f6cf6e13087c3ed#body_text
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