Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.
Identifieur interne : 000492 ( Main/Exploration ); précédent : 000491; suivant : 000493Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.
Auteurs : Eui Jin Hwang [Corée du Sud] ; Hyungjin Kim [Corée du Sud] ; Soon Ho Yoon [Corée du Sud] ; Jin Mo Goo [Corée du Sud] ; Chang Min Park [Corée du Sud]Source :
- Korean journal of radiology [ 2005-8330 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Apprentissage profond (MeSH), Betacoronavirus (MeSH), Femelle (MeSH), Humains (MeSH), Infections à coronavirus (imagerie diagnostique), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (imagerie diagnostique), Radiographie thoracique (méthodes), Sujet âgé (MeSH), Tomodensitométrie (méthodes), Études rétrospectives (MeSH).
- MESH :
- imagerie diagnostique : Infections à coronavirus, Pneumopathie virale.
- méthodes : Radiographie thoracique, Tomodensitométrie.
- Adulte, Adulte d'âge moyen, Apprentissage profond, Betacoronavirus, Femelle, Humains, Mâle, Pandémies, Sujet âgé, Études rétrospectives.
English descriptors
- KwdEn :
- Adult (MeSH), Aged (MeSH), Betacoronavirus (MeSH), Coronavirus Infections (diagnostic imaging), Deep Learning (MeSH), Female (MeSH), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnostic imaging), Radiography, Thoracic (methods), Retrospective Studies (MeSH), Tomography, X-Ray Computed (methods).
- MESH :
- diagnostic imaging : Coronavirus Infections, Pneumonia, Viral.
- methods : Radiography, Thoracic, Tomography, X-Ray Computed.
- Adult, Aged, Betacoronavirus, Deep Learning, Female, Humans, Male, Middle Aged, Pandemics, Retrospective Studies.
Abstract
OBJECTIVE
To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.
MATERIALS AND METHODS
In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated.
RESULTS
Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes;
CONCLUSION
Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.
DOI: 10.3348/kjr.2020.0536
PubMed: 32729263
PubMed Central: PMC7458860
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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COVID-19.</title><author><name sortKey="Hwang, Eui Jin" sort="Hwang, Eui Jin" uniqKey="Hwang E" first="Eui Jin" last="Hwang">Eui Jin Hwang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Department of Radiology, Seoul National University College of Medicine, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement><region type="capital">Région capitale de Séoul</region></placeName></affiliation></author><author><name sortKey="Kim, Hyungjin" sort="Kim, Hyungjin" uniqKey="Kim H" first="Hyungjin" last="Kim">Hyungjin Kim</name><affiliation wicri:level="3"><nlm:affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Department of Radiology, Seoul National University College of Medicine, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement><region type="capital">Région capitale de Séoul</region></placeName></affiliation></author><author><name sortKey="Yoon, Soon Ho" sort="Yoon, Soon Ho" uniqKey="Yoon S" first="Soon Ho" last="Yoon">Soon Ho Yoon</name><affiliation wicri:level="3"><nlm:affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Department of Radiology, Seoul National University College of Medicine, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement><region type="capital">Région capitale de Séoul</region></placeName></affiliation></author><author><name sortKey="Goo, Jin Mo" sort="Goo, Jin Mo" uniqKey="Goo J" first="Jin Mo" last="Goo">Jin Mo Goo</name><affiliation wicri:level="3"><nlm:affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Department of Radiology, Seoul National University College of Medicine, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement><region type="capital">Région capitale de Séoul</region></placeName></affiliation></author><author><name sortKey="Park, Chang Min" sort="Park, Chang Min" uniqKey="Park C" first="Chang Min" last="Park">Chang Min Park</name><affiliation wicri:level="3"><nlm:affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea. cmpark.morphius@gmail.com.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Department of Radiology, Seoul National University College of Medicine, Seoul</wicri:regionArea><placeName><settlement type="city">Séoul</settlement><region type="capital">Région capitale de Séoul</region></placeName></affiliation></author></analytic><series><title level="j">Korean journal of radiology</title><idno type="eISSN">2005-8330</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Coronavirus Infections (diagnostic imaging)</term><term>Deep Learning (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (diagnostic imaging)</term><term>Radiography, Thoracic (methods)</term><term>Retrospective Studies (MeSH)</term><term>Tomography, X-Ray Computed (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Apprentissage profond (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (imagerie diagnostique)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (imagerie diagnostique)</term><term>Radiographie thoracique (méthodes)</term><term>Sujet âgé (MeSH)</term><term>Tomodensitométrie (méthodes)</term><term>Études rétrospectives (MeSH)</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Radiography, Thoracic</term><term>Tomography, X-Ray Computed</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Radiographie thoracique</term><term>Tomodensitométrie</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Betacoronavirus</term><term>Deep Learning</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Pandemics</term><term>Retrospective Studies</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Apprentissage profond</term><term>Betacoronavirus</term><term>Femelle</term><term>Humains</term><term>Mâle</term><term>Pandémies</term><term>Sujet âgé</term><term>Études rétrospectives</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.</p></div><div type="abstract" xml:lang="en"><p><b>MATERIALS AND METHODS</b></p><p>In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; </p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32729263</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>04</Day></DateCompleted><DateRevised><Year>2020</Year><Month>10</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2005-8330</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>10</Issue><PubDate><Year>2020</Year><Month>10</Month></PubDate></JournalIssue><Title>Korean journal of radiology</Title><ISOAbbreviation>Korean J Radiol</ISOAbbreviation></Journal><ArticleTitle>Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.</ArticleTitle><Pagination><MedlinePgn>1150-1160</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3348/kjr.2020.0536</ELocationID><Abstract><AbstractText Label="OBJECTIVE">To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.</AbstractText><AbstractText Label="MATERIALS AND METHODS">In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated.</AbstractText><AbstractText Label="RESULTS">Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; <i>p</i> < 0.001).</AbstractText><AbstractText Label="CONCLUSION">Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.</AbstractText><CopyrightInformation>Copyright © 2020 The Korean Society of Radiology.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hwang</LastName><ForeName>Eui Jin</ForeName><Initials>EJ</Initials><Identifier Source="ORCID">0000-0002-3697-5542</Identifier><AffiliationInfo><Affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kim</LastName><ForeName>Hyungjin</ForeName><Initials>H</Initials><Identifier Source="ORCID">0000-0003-0722-0033</Identifier><AffiliationInfo><Affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yoon</LastName><ForeName>Soon Ho</ForeName><Initials>SH</Initials><Identifier Source="ORCID">0000-0002-3700-0165</Identifier><AffiliationInfo><Affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Goo</LastName><ForeName>Jin Mo</ForeName><Initials>JM</Initials><Identifier Source="ORCID">0000-0003-1791-7942</Identifier><AffiliationInfo><Affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Park</LastName><ForeName>Chang Min</ForeName><Initials>CM</Initials><Identifier Source="ORCID">0000-0003-1884-3738</Identifier><AffiliationInfo><Affiliation>Department of Radiology, Seoul National University College of Medicine, Seoul, Korea. cmpark.morphius@gmail.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>03-2019-0190</GrantID><Agency>SNUH</Agency><Country>International</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>Korea (South)</Country><MedlineTA>Korean J Radiol</MedlineTA><NlmUniqueID>100956096</NlmUniqueID><ISSNLinking>1229-6929</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000077321" MajorTopicYN="Y">Deep Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013902" MajorTopicYN="Y">Radiography, Thoracic</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014057" MajorTopicYN="N">Tomography, X-Ray Computed</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword><Keyword MajorTopicYN="Y">COVID-19 diagnostic testing</Keyword><Keyword MajorTopicYN="Y">Deep learning</Keyword><Keyword MajorTopicYN="Y">Pneumonia</Keyword><Keyword MajorTopicYN="Y">Radiography, thoracic</Keyword></KeywordList><CoiStatement>Eui Jin Hwang, Hyungjin Kim, and Chang Min Park report research grants from the Lunit Inc., outside the present study. Jin Mo Goo report research grant from the INFINITT Healthcare, outside the present study.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>05</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>7</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>9</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>7</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32729263</ArticleId><ArticleId IdType="pii">21.e106</ArticleId><ArticleId 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capitale de Séoul</li></region><settlement><li>Séoul</li></settlement></list><tree><country name="Corée du Sud"><region name="Région capitale de Séoul"><name sortKey="Hwang, Eui Jin" sort="Hwang, Eui Jin" uniqKey="Hwang E" first="Eui Jin" last="Hwang">Eui Jin Hwang</name></region><name sortKey="Goo, Jin Mo" sort="Goo, Jin Mo" uniqKey="Goo J" first="Jin Mo" last="Goo">Jin Mo Goo</name><name sortKey="Kim, Hyungjin" sort="Kim, Hyungjin" uniqKey="Kim H" first="Hyungjin" last="Kim">Hyungjin Kim</name><name sortKey="Park, Chang Min" sort="Park, Chang Min" uniqKey="Park C" first="Chang Min" last="Park">Chang Min Park</name><name sortKey="Yoon, Soon Ho" sort="Yoon, Soon Ho" uniqKey="Yoon S" first="Soon Ho" last="Yoon">Soon Ho Yoon</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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