https://m2.mtmt.hu/ hun 2026-10-23 06:13 JournalArticle 35580010 VALIDATED true 0 false 2026-07-20T20:01:11.525+0000 2024-11-19T12:35:04.236+0000 2024-11-19T12:29:14.390+0000 true 10080205 Szalóky-Siki Ágnes /api/admin/10080205 Admin true 2024-11-19T12:35:04.247+0000 2024-11-19T12:35:04.247+0000 2024-11-19T12:35:04.241+0000 true 10080205 Szalóky-Siki Ágnes /api/admin/10080205 Admin true true false true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType 101 true 10000059 true 1 /api/category/1 Category true 1 Boztuna, Mehmet Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study true true /api/journal/10016258 REVIEWED true false 10016258 false 10016258 true 1472-6831 1472-6831 Journal FOREIGN 24 1 1332 1332 8 2024 Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U-2-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U-2-Net architecture trained using a deep supervision algorithm. An AI model based on the U-2-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U-2-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models. true true 2024 true true true false false GOLD 2026-07-20 false http://www.biomedcentral.com/bmcoralhealth 29 0 29 27 0 29 29 22 26 29 29 27 27 27 2 0 29 27 29 27 0 0 0 Folyóiratcikk Journal Article 24 28 Könyvrészlet Chapter in Book 25 1 Könyv Book 23 0 Egyéb konferenciaközlemény Conference paper 31 0 Egyéb konferenciakötet Conference proceedings 32 0 Oltalmi formák Protection forms 26 0 Disszertáció Thesis 28 0 Egyéb Miscellaneous 29 0 Alkotás Achievement 22 0 Kutatási adat Research data 33 0 Folyóiratcikk Journal Article 24 0 0 0 Könyvrészlet Chapter in Book 25 0 0 0 Könyv Book 23 0 0 0 Egyéb konferenciaközlemény Conference paper 31 0 0 0 Egyéb konferenciakötet Conference proceedings 32 0 0 0 Oltalmi formák Protection forms 26 0 0 0 Disszertáció Thesis 28 0 0 0 Egyéb Miscellaneous 29 0 0 0 Alkotás Achievement 22 0 0 0 Kutatási adat Research data 33 0 0 0 2025 0 13 11 13 11 2026 0 16 16 16 16 Q1 false false false false 2024-11-18T12:35:04.142+0000 Orális Diagnosztikai Tanszék (SE / FOK) 1 4 1 4 0 true Language 10002 /api/language/10002 Angol English true 2 true PersonAuthorship 121685206 /api/authorship/121685206 1 0.25 true false false Boztuna Mehmet true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 121685207 /api/authorship/121685207 2 0.25 false false true Firincioglulari Mujgan true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 121685208 /api/authorship/121685208 3 0.25 false false false Akkaya Nurullah true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 121685209 /api/authorship/121685209 4 0.25 false true false Author 10092814 /api/author/10092814 Orhan Kaan true true Orhan Kaan true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PublicationIdentifier 27945338 /api/publicationidentifier/27945338 PlainSource 6 /api/publicationsource/6 PublicationSourceType 10001 /api/publicationsourcetype/10001 true true true DOI DOI https://doi.org/@@@ true true 6 true IDENTICAL 10.1186/s12903-024-05126-4 https://doi.org/10.1186/s12903-024-05126-4 false true PublicationIdentifier 27945339 /api/publicationidentifier/27945339 PlainSource 1 /api/publicationsource/1 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 001346565200007 https://www.webofscience.com/wos/woscc/full-record/001346565200007 false true PublicationIdentifier 27945341 /api/publicationidentifier/27945341 PlainSource 3 /api/publicationsource/3 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true Scopus Scopus http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-@@@ true true 3 true IDENTICAL 85208291597 http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85208291597 false true PublicationIdentifier 27945340 /api/publicationidentifier/27945340 PlainSource 17 /api/publicationsource/17 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true PubMed PubMed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=@@@&dopt=Abstract true true 17 true IDENTICAL 39487404 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=39487404&dopt=Abstract false true Classification 12262 /api/classification/12262 true true Keyword 3693 /api/keyword/3693 true 3693 true Keyword 5322 /api/keyword/5322 true 5322 true Keyword 1427322 /api/keyword/1427322 true 1427322 true Keyword 1994043 /api/keyword/1994043 true true SjrRating 11454928 /api/sjrrating/11454928 26 0.24 journal RatingType 10002 /api/ratingtype/10002 sjr true true ClassificationExternal 3501 /api/classificationexternal/3501 true 3501 true Q1 DIRECT true true /api/publication/35580010 <div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <div class="autype autype0"> <span class="author-name" >Boztuna Mehmet </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Firincioglulari Mujgan ✉ </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Akkaya Nurullah </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10092814"><a href="/gui2/?type=authors&mode=browse&sel=10092814" target="_blank">Orhan Kaan (<span class="authorship-author-name">Orhan Kaan</span> <span class="authorAux-mtmt"> Fogorvostudomány</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Fogorvostudományi Kar">FOK</span>/Orális Diagnosztikai Tanszék</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;35580010" target="_blank">Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study</a></div> <div> <span class="journal-title">BMC ORAL HEALTH</span> <span class="journal-issn">(<a target="_blank" href="https://portal.issn.org/resource/ISSN/1472-6831">1472-6831</a> <a target="_blank" href="https://portal.issn.org/resource/ISSN/1472-6831">1472-6831</a>)</span>: <span class="journal-volume">24</span> <span class="journal-issue">1</span> <span class="page"> Paper 1332. 8 p. </span> <span class="year">(2024)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1186/s12903-024-05126-4" target="_blank" href="https://doi.org/10.1186/s12903-024-05126-4"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="001346565200007" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/001346565200007"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="85208291597" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85208291597"> Scopus </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="39487404" target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=39487404&dopt=Abstract"> PubMed </a> </span> </span> <OnlyViewableByAuthor><div class="ratings"> <div class="journal-subject">Folyóirat szakterülete: Scopus - Dentistry (miscellaneous)&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q1</div> </div></OnlyViewableByAuthor> <div class="publication-citation" style="margin-left: 0.5cm;"> <span title="Nyilvános idézőközlemények összesen, említések nélkül" class="citingPub-count">Nyilvános idéző összesen: 29</span> | Független: 27 | Függő: 2 | Nem jelölt: 0 | WoS jelölt: 22 | Scopus jelölt:&nbsp;26 | WoS/Scopus jelölt:&nbsp;29 | DOI jelölt:&nbsp;29 </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 35580010</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> Forrás | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">WOS TAGGED</span> </div> <div class="lastModified">Utolsó módosítás: 2024.11.19. 13:35 Szalóky-Siki Ágnes (SE_KK_Admin5_SZSA, admin) </div> </div></div>