{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-VBRA61WV/3b3035e1-3453-4692-b390-19bc5eda4fa2/PDF","dcterms:extent":"2975 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-VBRA61WV/00bae829-f49a-417d-802f-22c8c3fe43f3/TEXT","dcterms:extent":"28 KB"}],"edm:TimeSpan":{"@rdf:about":"2002-2024","edm:begin":{"@xml:lang":"en","#text":"2002"},"edm:end":{"@xml:lang":"en","#text":"2024"}},"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:doc-VBRA61WV","dcterms:isPartOf":[{"@rdf:resource":"https://www.dlib.si/details/URN:NBN:SI:spr-1R5DVDOA"},{"@xml:lang":"sl","#text":"Informatica Medica Slovenica"}],"dcterms:issued":"2024","dc:creator":["Bizjak, Žiga","Robič, Tina"],"dc:format":[{"@xml:lang":"sl","#text":"številka:1"},{"@xml:lang":"sl","#text":"letnik:29"},{"@xml:lang":"sl","#text":"str. 1-7"}],"dc:identifier":["ISSN:1318-2129","COBISSID_HOST:212814083","URN:URN:NBN:SI:doc-VBRA61WV"],"dc:language":"en","dc:publisher":{"@xml:lang":"sl","#text":"Slovensko društvo za medicinsko informatiko"},"dc:subject":[{"@xml:lang":"en","#text":"caries"},{"@xml:lang":"en","#text":"deep learning"},{"@xml:lang":"en","#text":"detection"},{"@xml:lang":"sl","#text":"detekcija"},{"@xml:lang":"sl","#text":"globoko učenje"},{"@xml:lang":"sl","#text":"karies"},{"@xml:lang":"sl","#text":"segmentacija"},{"@xml:lang":"en","#text":"segmentation"},{"@xml:lang":"sl","#text":"YOLOv8"}],"dcterms:temporal":{"@rdf:resource":"2002-2024"},"dc:title":{"@xml:lang":"sl","#text":"Advancing dental caries detection in panoramic xrays| a two-step deep learning approach| Odkrivanje zobnega kariesa na ortopantomogramih| pristop v dveh korakih z uporabo globokega učenja|"},"dc:description":[{"@xml:lang":"sl","#text":"Caries, preventable but common oral disease, can cause tooth pain and tooth loss. Early detection is key for timely treatment. To improve caries detection, we employed a two-step deep learning algorithm, specifically adapting the YOLOv8 computer vision model to analyse panoramic X-rays. Firstly, the Teeth Enumeration Model (TEM) is trained to enumerate teeth following the Fédération Dentaire Internationale system. Subsequently, the Caries Detection Model (CDM), built upon the TEM, focuses on detecting caries lesions. We used clinically relevant metrics to evaluate the model, including lesion-level sensitivity, patient-level specificity, and number of false positives per image. The TEM demonstrated strong performance, achieving a high mAP0.5 value of 0.95, indicating accurate tooth labelling. The subsequent CDM exhibited satisfactory lesion-level sensitivity in both internal (0.70) and external (0.67) validations. A comparison with previous studies, including the DENTEX challenge winner, highlights the superiority of the proposed approach. This research contributes to the advancement of dental caries detection through a robust algorithm and emphasises the potential of YOLOv8 in automating tooth labelling and caries detection"},{"@xml:lang":"sl","#text":"Karies, ki je preprečljiva, a pogosta bolezen ustne votline, lahko povzroči bolečine in izgubo zob. Njegovo zgodnje odkrivanje je ključno za pravočasno zdravljenje. Za izboljšanje odkrivanja kariesa na panoramskih rentgenskih posnetkih smo uporabili dvostopenjski algoritem globokega učenja, ki temelji na prilagojenem modelu YOLOv8. V prvem koraku smo naučili in uporabili model za označevanje zob (TEM) po sistemu Mednarodne zveze zobozdravnikov. V drugem koraku pa smo na osnovi modela TEM naučili še model za odkrivanje kariesa (CDM), ki je osredotočen na odkrivanje karioznih lezij. Za ocenjevanje modela smo uporabili klinično relevantne mere, vključno z občutljivostjo na ravni lezije, specifičnostjo na ravni pacienta in številom lažno pozitivnih primerov na sliko. Model TEM je pokazal močno uspešnost, saj je dosegel visoko vrednost mAP0.5 (0,95), kar kaže na natančno označevanje zob. Model CDM je pokazal zadovoljivo občutljivost na ravni lezije tako v notranjih (0,70) kot v zunanjih (0,67) validacijskih množicah. Primerjava s prejšnjimi študijami, vključno z zmagovalcem izziva DENTEX, kaže na superiornost predlaganega pristopa. Raziskava prispeva k napredku pri avtomatskem odkrivanju zobnih kariesov z uporabo robustnega algoritma in poudarja potencial YOLOv8 pri avtomatizaciji označevanja zob in odkrivanju kariesa"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:doc-VBRA61WV","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:doc-VBRA61WV"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:doc-VBRA61WV/3b3035e1-3453-4692-b390-19bc5eda4fa2/PDF"},"edm:rights":{"@rdf:resource":"http://rightsstatements.org/vocab/InC/1.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Slovensko društvo za medicinsko informatiko"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:doc-VBRA61WV/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:doc-VBRA61WV"}}}}