About: Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence 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
has title	Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence
Creator	Dphil, Prokop, Mathias Schaefer-Prokop, Cornelia Boulogne, H Bram De Wilde, ; Bram Van Ginneken, ; Brink, Monique Calli, Erdi Charbonnier, Jean-Paul Coen De Vente, ; Dofferhoff, Ton Ernst, ; Gerke, Paul Geurts, Bram Gietema, Hester Groeneveld, Miriam Hendrix, ; Hendrix, Nils Huisman, Henkjan Išgum, Ivana Jacobs, Colin Kicky Van Leeuwen, ; Kiran, ; Kluge, Ruben Kok, Michel Krdzalic, Jasenko Lassen-Schmidt, Bianca Lessmann, Nikolas Louis Van Harten, ; Ludo Beenen, ; Luuk, ; Meakin, James Overkamp, Mike Samperna, Riccardo Schalekamp, Steven Scholten, Th Sital, Cheryl Stöger, Lauran Sánchez, Clara Teuwen, Jonas Tjalco, ; Van Everdingen, M Van Ginneken, Bram Van Rikxoort, Eva Vellinga, Rees Venkadesh, Vaidhya Vermaat, Marieke Wouter, ; Xie, Weiyi
Source	Medline; PMC
abstract	BACKGROUND: The COVID-19 pandemic has spread across the globe with alarming speed, morbidity and mortality. Immediate triage of suspected patients with chest infections caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. PURPOSE: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the CO-RADS and CT severity scoring systems. MATERIALS AND METHODS: CORADS-AI consists of three deep learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19 and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who received an unenhanced chest CT scan due to clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic (ROC) analysis, linearly-weighted kappa and classification accuracy. RESULTS: 105 patients (62 ± 16 years, 61 men) and 262 patients (64 ± 16 years, 154 men) were evaluated in the internal and the external test set, respectively. The system discriminated between COVID-19 positive and negative patients with areas under the ROC curve of 0.95 (95% CI: 0.91-0.98) and 0.88 (95% CI: 0.84-0.93). Agreement with the eight human observers was moderate to substantial with a mean linearly-weighted kappa of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. CONCLUSION: CORADS-AI correctly identified COVID-19 positive patients with high diagnostic performance from chest CT exams, assigned standardized CO-RADS and CT severity scores in good agreement with eight independent observers and generalized well to external data.
has issue date	2020-07-30(xsd:dateTime)
bibo:doi	10.1148/radiol.2020202439
bibo:pmid	32729810
has license	no-cc
sha1sum (hex)	77d2998052da2dce4efd48dbc009b20cc81e674e
schema:url	https://doi.org/10.1148/radiol.2020202439
resource representing a document's title	Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence
has PubMed Central identifier	PMC7393955
has PubMed identifier	32729810
schema:publication	Radiology
resource representing a document's body	covid:77d2998052da2dce4efd48dbc009b20cc81e674e#body_text
is schema:about of	named entity 'Word count' named entity 'Artificial Intelligence' named entity 'validation set' named entity 'radiologists' named entity 'CT scans' named entity 'chest CT' named entity 'CT image' named entity 'teaching hospital' named entity 'CTSS' named entity 'COVID' named entity 'CT scans' named entity 'standard deviation' named entity 'medical center' named entity 'teaching hospital' named entity 'informed consent' named entity 'chi-squared' named entity 'CT scans' named entity 'CT imaging' named entity '95% CI' named entity 'COVID' named entity 'ImageNet' named entity 'Monte-Carlo' named entity 'radiologists' named entity 'CTSS' named entity 'ground-glass opacities' named entity 'confidence intervals' named entity 'lung tissue' named entity 'RT-PCR' named entity 'image classification' named entity 'algorithm' named entity 'COVID' named entity 'parenchymal' named entity 'CTSS'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software