Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis
We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning...
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| Veröffentlicht in: | Scientific reports 2020-05, Vol.10 (1), p.7531-7531, Article 7531 |
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| creator | Chang, Hyuk-Joon Lee, Sang-Jeong Yong, Tae-Hoon Shin, Nan-Young Jang, Bong-Geun Kim, Jo-Eun Huh, Kyung-Hoe Lee, Sam-Sun Heo, Min-Suk Choi, Soon-Chul Kim, Tae-Il Yi, Won-Jin |
| description | We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was
0.73
overall for the whole jaw (p |
| doi_str_mv | 10.1038/s41598-020-64509-z |
| format | Article |
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0.73
overall for the whole jaw (p < 0.01), and the intraclass correlation value
0.91
overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-64509-z</identifier><identifier>PMID: 32372049</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>639/166/985 ; 692/699/3020/3029 ; Algorithms ; Alveolar Bone Loss - diagnostic imaging ; Bone loss ; Classification ; Correlation coefficient ; Deep Learning ; Diagnosis, Computer-Assisted - methods ; Gum disease ; Humanities and Social Sciences ; Humans ; Image Processing, Computer-Assisted - methods ; Jaw ; Mandible - diagnostic imaging ; Maxilla - diagnostic imaging ; multidisciplinary ; Pattern Recognition, Automated ; Periodontitis ; Periodontitis - diagnosis ; Radiography ; Reproducibility of Results ; Science ; Science (multidisciplinary) ; Teeth</subject><ispartof>Scientific reports, 2020-05, Vol.10 (1), p.7531-7531, Article 7531</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-a37f8aad4bd1e6446ccc5672d68f821b6f546e38c85fdec942701168e6ec03e3</citedby><cites>FETCH-LOGICAL-c540t-a37f8aad4bd1e6446ccc5672d68f821b6f546e38c85fdec942701168e6ec03e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200807/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200807/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,41120,42189,51576,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32372049$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chang, Hyuk-Joon</creatorcontrib><creatorcontrib>Lee, Sang-Jeong</creatorcontrib><creatorcontrib>Yong, Tae-Hoon</creatorcontrib><creatorcontrib>Shin, Nan-Young</creatorcontrib><creatorcontrib>Jang, Bong-Geun</creatorcontrib><creatorcontrib>Kim, Jo-Eun</creatorcontrib><creatorcontrib>Huh, Kyung-Hoe</creatorcontrib><creatorcontrib>Lee, Sam-Sun</creatorcontrib><creatorcontrib>Heo, Min-Suk</creatorcontrib><creatorcontrib>Choi, Soon-Chul</creatorcontrib><creatorcontrib>Kim, Tae-Il</creatorcontrib><creatorcontrib>Yi, Won-Jin</creatorcontrib><title>Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was
0.73
overall for the whole jaw (p < 0.01), and the intraclass correlation value
0.91
overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.</description><subject>639/166/985</subject><subject>692/699/3020/3029</subject><subject>Algorithms</subject><subject>Alveolar Bone Loss - diagnostic imaging</subject><subject>Bone loss</subject><subject>Classification</subject><subject>Correlation coefficient</subject><subject>Deep Learning</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Gum disease</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Jaw</subject><subject>Mandible - diagnostic imaging</subject><subject>Maxilla - diagnostic imaging</subject><subject>multidisciplinary</subject><subject>Pattern Recognition, Automated</subject><subject>Periodontitis</subject><subject>Periodontitis - diagnosis</subject><subject>Radiography</subject><subject>Reproducibility of Results</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Teeth</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU9rFTEUxYMottR-ARcScONmNP8n2Qi1VSs8UbD7kJe5M02ZlzyTjPD66U19tT5dmE0C53fPzb0HoeeUvKaE6zdFUGl0RxjplJDEdLeP0DEjQnaMM_b44H2ETku5Ie1IZgQ1T9ERZ7xvujlG_gJgi1fgcgxxwpe7dQ4D_gz1Og24Jny21LRxNXg3zzt8EdwUUwH8FXJIQ4rVzfhdioBXqRTs4oC_VTcd6KGG8gw9Gd1c4PT-PkFXH95fnV92qy8fP52frTovBamd4_2onRvEeqCghFDee6l6Nig9akbXapRCAddey3EAbwTrCaVKgwJPOPAT9HZvu13WGxg8xJrdbLc5bFze2eSC_VuJ4dpO6YdtqyCa9M3g1b1BTt8XKNVuQvEwzy5CWopl3BgmOFOqoS__QW_SkmOb7o7SUmlmZKPYnvK5rSfD-PAZSuxdinafom0p2l8p2ttW9OJwjIeS35k1gO-B0qQ4Qf7T-z-2PwGl3Km6</recordid><startdate>20200505</startdate><enddate>20200505</enddate><creator>Chang, Hyuk-Joon</creator><creator>Lee, Sang-Jeong</creator><creator>Yong, Tae-Hoon</creator><creator>Shin, Nan-Young</creator><creator>Jang, Bong-Geun</creator><creator>Kim, Jo-Eun</creator><creator>Huh, Kyung-Hoe</creator><creator>Lee, Sam-Sun</creator><creator>Heo, Min-Suk</creator><creator>Choi, Soon-Chul</creator><creator>Kim, Tae-Il</creator><creator>Yi, Won-Jin</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200505</creationdate><title>Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis</title><author>Chang, Hyuk-Joon ; Lee, Sang-Jeong ; Yong, Tae-Hoon ; Shin, Nan-Young ; Jang, Bong-Geun ; Kim, Jo-Eun ; Huh, Kyung-Hoe ; Lee, Sam-Sun ; Heo, Min-Suk ; Choi, Soon-Chul ; Kim, Tae-Il ; Yi, Won-Jin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c540t-a37f8aad4bd1e6446ccc5672d68f821b6f546e38c85fdec942701168e6ec03e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>639/166/985</topic><topic>692/699/3020/3029</topic><topic>Algorithms</topic><topic>Alveolar Bone Loss - diagnostic imaging</topic><topic>Bone loss</topic><topic>Classification</topic><topic>Correlation coefficient</topic><topic>Deep Learning</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Gum disease</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Jaw</topic><topic>Mandible - diagnostic imaging</topic><topic>Maxilla - diagnostic imaging</topic><topic>multidisciplinary</topic><topic>Pattern Recognition, Automated</topic><topic>Periodontitis</topic><topic>Periodontitis - diagnosis</topic><topic>Radiography</topic><topic>Reproducibility of Results</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Teeth</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Hyuk-Joon</creatorcontrib><creatorcontrib>Lee, Sang-Jeong</creatorcontrib><creatorcontrib>Yong, Tae-Hoon</creatorcontrib><creatorcontrib>Shin, Nan-Young</creatorcontrib><creatorcontrib>Jang, Bong-Geun</creatorcontrib><creatorcontrib>Kim, Jo-Eun</creatorcontrib><creatorcontrib>Huh, Kyung-Hoe</creatorcontrib><creatorcontrib>Lee, Sam-Sun</creatorcontrib><creatorcontrib>Heo, Min-Suk</creatorcontrib><creatorcontrib>Choi, Soon-Chul</creatorcontrib><creatorcontrib>Kim, Tae-Il</creatorcontrib><creatorcontrib>Yi, Won-Jin</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Hyuk-Joon</au><au>Lee, Sang-Jeong</au><au>Yong, Tae-Hoon</au><au>Shin, Nan-Young</au><au>Jang, Bong-Geun</au><au>Kim, Jo-Eun</au><au>Huh, Kyung-Hoe</au><au>Lee, Sam-Sun</au><au>Heo, Min-Suk</au><au>Choi, Soon-Chul</au><au>Kim, Tae-Il</au><au>Yi, Won-Jin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2020-05-05</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><spage>7531</spage><epage>7531</epage><pages>7531-7531</pages><artnum>7531</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was
0.73
overall for the whole jaw (p < 0.01), and the intraclass correlation value
0.91
overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32372049</pmid><doi>10.1038/s41598-020-64509-z</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 639/166/985 692/699/3020/3029 Algorithms Alveolar Bone Loss - diagnostic imaging Bone loss Classification Correlation coefficient Deep Learning Diagnosis, Computer-Assisted - methods Gum disease Humanities and Social Sciences Humans Image Processing, Computer-Assisted - methods Jaw Mandible - diagnostic imaging Maxilla - diagnostic imaging multidisciplinary Pattern Recognition, Automated Periodontitis Periodontitis - diagnosis Radiography Reproducibility of Results Science Science (multidisciplinary) Teeth |
| title | Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis |
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