About: COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings

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About: COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings Goto Sponge NotDistinct Permalink

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
title	COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
Creator	Ardakani, Ali Habibollahi, Sina Mohammadi, Afshin Rajendra Acharya, &
source	Medline; PMC
abstract	OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-07087-y) contains supplementary material, which is available to authorized users.
has issue date	2020-08-01(xsd:dateTime)
bibo:doi	10.1007/s00330-020-07087-y
bibo:pmid	32740817
has license	no-cc
sha1sum (hex)	72b04017014620a5eabc36c75e832cf8975c8e28
schema:url	https://doi.org/10.1007/s00330-020-07087-y
resource representing a document's title	COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
has PubMed Central identifier	PMC7395802
has PubMed identifier	32740817
schema:publication	Eur Radiol
resource representing a document's body	covid:72b04017014620a5eabc36c75e832cf8975c8e28#body_text
is schema:about of	named entity 'performing' named entity 'reticular' named entity 'CAD' named entity 'radiological' named entity 'naïve Bayes' named entity 'location' named entity 'features' named entity 'classifiers' covid:arg/72b04017014620a5eabc36c75e832cf8975c8e28 named entity 'distribution' named entity 'consolidation' named entity 'thickening' named entity 'discriminate' named entity 'K-nearest neighbor' named entity 'images' named entity 'COVID-19' named entity 'classifier' named entity 'features' named entity 'support vector machine' named entity 'system' named entity 'COVID-19' named entity 'COVID-19' named entity 'COVID-19' named entity 'CAD' named entity 'classifier' named entity 'sensitivity, specificity' named entity 'lymphadenopathy' named entity 'K-nearest neighbor' named entity 'COVID-19' named entity 'ground-glass opacity' named entity 'CAD' named entity 'Radiologists' named entity 'COVID' named entity 'GGO' named entity 'chest CT' named entity 'decision tree' named entity 'CAD' named entity 'RT-PCR' named entity 'MDCT' named entity 'RT-PCR' named entity 'radiologist' named entity 'pneumonia' named entity 'COVID' named entity 'COVID' named entity 'lung' named entity 'decision tree' named entity 'COVID' named entity 'pneumonia' named entity 'COVID' named entity 'lesion' named entity 'COVID-19' named entity 'pneumonia' named entity 'COVID-19' named entity 'CAD' named entity 'COVID' named entity 'RT-PCR' named entity 'cough' named entity 'cross-validation' named entity 'cross-sectional study' named entity 'RT-PCR' named entity 'bronchi' named entity 'COVID' named entity 'bronchial wall thickening' named entity 'air bronchogram' named entity 'naïve Bayes' named entity 'RT-PCR' named entity 'laboratory test' named entity 'nodule' named entity 'coronavirus' named entity 'pneumonia' named entity 'radiologist' named entity 'chi-square test' named entity 'opacity' named entity 'radiologists' named entity 'COVID' named entity 'learning process' named entity 'COVID' named entity 'Air bronchogram'

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