Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.
Identifieur interne : 000069 ( Main/Exploration ); précédent : 000068; suivant : 000070Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.
Auteurs : Makoto Murata [Japon] ; Yoshiko Ariji [Japon] ; Yasufumi Ohashi [Japon] ; Taisuke Kawai [Japon] ; Motoki Fukuda [Japon] ; Takuma Funakoshi [Japon] ; Yoshitaka Kise [Japon] ; Michihito Nozawa [Japon] ; Akitoshi Katsumata [Japon] ; Hiroshi Fujita [Japon] ; Eiichiro Ariji [Japon]Source :
- Oral radiology [ 1613-9674 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- imagerie diagnostique : Sinusite maxillaire.
- Aire sous la courbe, Apprentissage profond, Humains, Radiographie panoramique.
English descriptors
- KwdEn :
- MESH :
- diagnostic imaging : Maxillary Sinusitis.
- Area Under Curve, Deep Learning, Humans, Neural Networks, Computer, Radiography, Panoramic.
Abstract
OBJECTIVES
To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance.
METHODS
Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents.
RESULTS
The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents.
CONCLUSIONS
The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.
DOI: 10.1007/s11282-018-0363-7
PubMed: 30539342
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. yoshiko@dpc.agu.ac.jp.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Ohashi, Yasufumi" sort="Ohashi, Yasufumi" uniqKey="Ohashi Y" first="Yasufumi" last="Ohashi">Yasufumi Ohashi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 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Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Funakoshi, Takuma" sort="Funakoshi, Takuma" uniqKey="Funakoshi T" first="Takuma" last="Funakoshi">Takuma Funakoshi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Kise, Yoshitaka" sort="Kise, Yoshitaka" uniqKey="Kise Y" first="Yoshitaka" last="Kise">Yoshitaka Kise</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Nozawa, Michihito" sort="Nozawa, Michihito" uniqKey="Nozawa M" first="Michihito" last="Nozawa">Michihito Nozawa</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Katsumata, Akitoshi" sort="Katsumata, Akitoshi" uniqKey="Katsumata A" first="Akitoshi" last="Katsumata">Akitoshi Katsumata</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral Radiology, Asahi University School of Dentistry, Mizuho</wicri:regionArea><wicri:noRegion>Mizuho</wicri:noRegion></affiliation></author><author><name sortKey="Fujita, Hiroshi" sort="Fujita, Hiroshi" uniqKey="Fujita H" first="Hiroshi" last="Fujita">Hiroshi Fujita</name><affiliation wicri:level="4"><nlm:affiliation>Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu</wicri:regionArea><orgName type="university">Université de Gifu</orgName><placeName><settlement type="city">Gifu (ville)</settlement><region type="province">Région du Chūbu</region><region type="prefecture">Préfecture de Gifu</region></placeName></affiliation></author><author><name sortKey="Ariji, Eiichiro" sort="Ariji, Eiichiro" uniqKey="Ariji E" first="Eiichiro" last="Ariji">Eiichiro Ariji</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:30539342</idno><idno type="pmid">30539342</idno><idno type="doi">10.1007/s11282-018-0363-7</idno><idno type="wicri:Area/Main/Corpus">000074</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000074</idno><idno type="wicri:Area/Main/Curation">000074</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000074</idno><idno type="wicri:Area/Main/Exploration">000074</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.</title><author><name sortKey="Murata, Makoto" sort="Murata, Makoto" uniqKey="Murata M" first="Makoto" last="Murata">Makoto Murata</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Ariji, Yoshiko" sort="Ariji, Yoshiko" uniqKey="Ariji Y" first="Yoshiko" last="Ariji">Yoshiko Ariji</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. yoshiko@dpc.agu.ac.jp.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Ohashi, Yasufumi" sort="Ohashi, Yasufumi" uniqKey="Ohashi Y" first="Yasufumi" last="Ohashi">Yasufumi Ohashi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Kawai, Taisuke" sort="Kawai, Taisuke" uniqKey="Kawai T" first="Taisuke" last="Kawai">Taisuke Kawai</name><affiliation wicri:level="3"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo</wicri:regionArea><placeName><settlement type="city">Tokyo</settlement><region type="région">Région de Kantō</region></placeName></affiliation></author><author><name sortKey="Fukuda, Motoki" sort="Fukuda, Motoki" uniqKey="Fukuda M" first="Motoki" last="Fukuda">Motoki Fukuda</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Funakoshi, Takuma" sort="Funakoshi, Takuma" uniqKey="Funakoshi T" first="Takuma" last="Funakoshi">Takuma Funakoshi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Kise, Yoshitaka" sort="Kise, Yoshitaka" uniqKey="Kise Y" first="Yoshitaka" last="Kise">Yoshitaka Kise</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Nozawa, Michihito" sort="Nozawa, Michihito" uniqKey="Nozawa M" first="Michihito" last="Nozawa">Michihito Nozawa</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author><author><name sortKey="Katsumata, Akitoshi" sort="Katsumata, Akitoshi" uniqKey="Katsumata A" first="Akitoshi" last="Katsumata">Akitoshi Katsumata</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral Radiology, Asahi University School of Dentistry, Mizuho</wicri:regionArea><wicri:noRegion>Mizuho</wicri:noRegion></affiliation></author><author><name sortKey="Fujita, Hiroshi" sort="Fujita, Hiroshi" uniqKey="Fujita H" first="Hiroshi" last="Fujita">Hiroshi Fujita</name><affiliation wicri:level="4"><nlm:affiliation>Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu</wicri:regionArea><orgName type="university">Université de Gifu</orgName><placeName><settlement type="city">Gifu (ville)</settlement><region type="province">Région du Chūbu</region><region type="prefecture">Préfecture de Gifu</region></placeName></affiliation></author><author><name sortKey="Ariji, Eiichiro" sort="Ariji, Eiichiro" uniqKey="Ariji E" first="Eiichiro" last="Ariji">Eiichiro Ariji</name><affiliation wicri:level="1"><nlm:affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651</wicri:regionArea><wicri:noRegion>464-8651</wicri:noRegion></affiliation></author></analytic><series><title level="j">Oral radiology</title><idno type="eISSN">1613-9674</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Area Under Curve (MeSH)</term><term>Deep Learning (MeSH)</term><term>Humans (MeSH)</term><term>Maxillary Sinusitis (diagnostic imaging)</term><term>Neural Networks, Computer (MeSH)</term><term>Radiography, Panoramic (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Aire sous la courbe (MeSH)</term><term>Apprentissage profond (MeSH)</term><term>Humains (MeSH)</term><term>Radiographie panoramique (MeSH)</term><term>Sinusite maxillaire (imagerie diagnostique)</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Maxillary Sinusitis</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Sinusite maxillaire</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Area Under Curve</term><term>Deep Learning</term><term>Humans</term><term>Neural Networks, Computer</term><term>Radiography, Panoramic</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Aire sous la courbe</term><term>Apprentissage profond</term><term>Humains</term><term>Radiographie panoramique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVES</b></p><p>To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">30539342</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>07</Day></DateCompleted><DateRevised><Year>2020</Year><Month>08</Month><Day>07</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1613-9674</ISSN><JournalIssue CitedMedium="Internet"><Volume>35</Volume><Issue>3</Issue><PubDate><Year>2019</Year><Month>09</Month></PubDate></JournalIssue><Title>Oral radiology</Title><ISOAbbreviation>Oral Radiol</ISOAbbreviation></Journal><ArticleTitle>Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.</ArticleTitle><Pagination><MedlinePgn>301-307</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s11282-018-0363-7</ELocationID><Abstract><AbstractText Label="OBJECTIVES">To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance.</AbstractText><AbstractText Label="METHODS">Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents.</AbstractText><AbstractText Label="RESULTS">The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents.</AbstractText><AbstractText Label="CONCLUSIONS">The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Murata</LastName><ForeName>Makoto</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ariji</LastName><ForeName>Yoshiko</ForeName><Initials>Y</Initials><Identifier Source="ORCID">0000-0002-7079-0867</Identifier><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. yoshiko@dpc.agu.ac.jp.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ohashi</LastName><ForeName>Yasufumi</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kawai</LastName><ForeName>Taisuke</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fukuda</LastName><ForeName>Motoki</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Funakoshi</LastName><ForeName>Takuma</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kise</LastName><ForeName>Yoshitaka</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nozawa</LastName><ForeName>Michihito</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Katsumata</LastName><ForeName>Akitoshi</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fujita</LastName><ForeName>Hiroshi</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ariji</LastName><ForeName>Eiichiro</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>12</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>Japan</Country><MedlineTA>Oral Radiol</MedlineTA><NlmUniqueID>8806621</NlmUniqueID><ISSNLinking>0911-6028</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D019540" MajorTopicYN="N">Area Under Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000077321" MajorTopicYN="Y">Deep Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015523" MajorTopicYN="Y">Maxillary Sinusitis</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="N">diagnostic imaging</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="Y">Neural Networks, Computer</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011862" MajorTopicYN="Y">Radiography, Panoramic</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Artificial intelligence</Keyword><Keyword MajorTopicYN="Y">Computed tomography</Keyword><Keyword MajorTopicYN="Y">Deep learning</Keyword><Keyword MajorTopicYN="Y">Maxillary sinusitis</Keyword><Keyword MajorTopicYN="Y">Panoramic radiography</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>09</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>11</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>12</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>12</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30539342</ArticleId><ArticleId IdType="doi">10.1007/s11282-018-0363-7</ArticleId><ArticleId IdType="pii">10.1007/s11282-018-0363-7</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Insights Imaging. 2015 Feb;6(1):1-16</Citation><ArticleIdList><ArticleId IdType="pubmed">25575868</ArticleId></ArticleIdList></Reference><Reference><Citation>Radiology. 2018 Mar;286(3):887-896</Citation><ArticleIdList><ArticleId IdType="pubmed">29059036</ArticleId></ArticleIdList></Reference><Reference><Citation>Radiology. 2018 Mar;286(3):845-852</Citation><ArticleIdList><ArticleId IdType="pubmed">29135365</ArticleId></ArticleIdList></Reference><Reference><Citation>J Endod. 2011 Jun;37(6):753-7</Citation><ArticleIdList><ArticleId 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Aug;23(3):149-53</Citation><ArticleIdList><ArticleId IdType="pubmed">7835515</ArticleId></ArticleIdList></Reference><Reference><Citation>EJNMMI Res. 2017 Dec;7(1):11</Citation><ArticleIdList><ArticleId IdType="pubmed">28130689</ArticleId></ArticleIdList></Reference><Reference><Citation>Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2004 Aug;98(2):223-31</Citation><ArticleIdList><ArticleId IdType="pubmed">15316549</ArticleId></ArticleIdList></Reference><Reference><Citation>Insights Imaging. 2018 Aug;9(4):611-629</Citation><ArticleIdList><ArticleId IdType="pubmed">29934920</ArticleId></ArticleIdList></Reference><Reference><Citation>Radiology. 2018 Feb;286(2):676-684</Citation><ArticleIdList><ArticleId IdType="pubmed">28925823</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Japon</li></country><region><li>Préfecture de Gifu</li><li>Région de Kantō</li><li>Région du Chūbu</li></region><settlement><li>Gifu (ville)</li><li>Tokyo</li></settlement><orgName><li>Université de Gifu</li></orgName></list><tree><country name="Japon"><noRegion><name sortKey="Murata, Makoto" sort="Murata, Makoto" uniqKey="Murata M" first="Makoto" last="Murata">Makoto Murata</name></noRegion><name sortKey="Ariji, Eiichiro" sort="Ariji, Eiichiro" uniqKey="Ariji E" first="Eiichiro" last="Ariji">Eiichiro Ariji</name><name sortKey="Ariji, Yoshiko" sort="Ariji, Yoshiko" uniqKey="Ariji Y" first="Yoshiko" last="Ariji">Yoshiko Ariji</name><name sortKey="Fujita, Hiroshi" sort="Fujita, Hiroshi" uniqKey="Fujita H" first="Hiroshi" last="Fujita">Hiroshi Fujita</name><name sortKey="Fukuda, Motoki" sort="Fukuda, Motoki" uniqKey="Fukuda M" first="Motoki" last="Fukuda">Motoki Fukuda</name><name sortKey="Funakoshi, Takuma" sort="Funakoshi, Takuma" uniqKey="Funakoshi T" first="Takuma" last="Funakoshi">Takuma Funakoshi</name><name sortKey="Katsumata, Akitoshi" sort="Katsumata, Akitoshi" uniqKey="Katsumata A" first="Akitoshi" last="Katsumata">Akitoshi Katsumata</name><name sortKey="Kawai, Taisuke" sort="Kawai, Taisuke" uniqKey="Kawai T" first="Taisuke" last="Kawai">Taisuke Kawai</name><name sortKey="Kise, Yoshitaka" sort="Kise, Yoshitaka" uniqKey="Kise Y" first="Yoshitaka" last="Kise">Yoshitaka Kise</name><name sortKey="Nozawa, Michihito" sort="Nozawa, Michihito" uniqKey="Nozawa M" first="Michihito" last="Nozawa">Michihito Nozawa</name><name sortKey="Ohashi, Yasufumi" sort="Ohashi, Yasufumi" uniqKey="Ohashi Y" first="Yasufumi" last="Ohashi">Yasufumi Ohashi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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