Deep-learning model for assessing difficulty in localizing impacted canines

Basit Öğe Kaydı
dc.authorid0009-0004-6316-6072
dc.authorid0000-0001-9840-4211
dc.authorid0000-0003-4966-9779
dc.contributor.authorÖzcan, Mustafa
dc.contributor.authorErdem, Buket
dc.contributor.authorTuran, Begüm
dc.contributor.authorTokatlı, Nazlı
dc.contributor.authorŞar, Çağla
dc.contributor.authorÖzdemir, Fulya
dc.date.accessioned2024-11-04T08:08:39Z
dc.date.available2024-11-04T08:08:39Z
dc.date.issued2024
dc.departmentFakülteler, Diş Hekimliği Fakültesi, Klinik Bilimler Bölümü, Ortodonti Ana Bilim Dalı
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAIM or PURPOSE The aim of this study is to examine if an artificial intelligence algorithm can be used for identifying the bucco-palatinal position of the maxillary impacted canine from the panoramic X-rays. MATERIALS and METHOD A total of 810 panoramic x-rays were obtained from the archive of University, Faculty of Dentistry, Department of Orthodontics. X-rays included cases with unilateral/bilaterally impacted canines. We used a Convolutional Neural Network (CNN) to forecast where the impacted canines crown would be situated. CNNs excel at classifying images as they can autonomously and flexibly grasp hierarchies of features, from input images. The implementation of the proposed deep learning model has been done using the Python programming language and libraries (TensorFlow and Numpy). The dataset that was used to train the proposed model categorized into buccal, middle, and palatinal positioned samples. These samples are mainly 2D panoramic X-rays combined with clinical information about the location of the canine. The expectation from the model is to determine the location of the crown of the maxillary-impacted canine. The model's prediction of the impacted canine's location was assessed in bucco-palatinal directions. RESULTS The proposed deep learning model predicts the position of the impacted canine in the buccal, middle, or palatinal position with 68% accuracy. CONCLUSION(S) This multidisciplinary research study developed a deep learning model to automate the detection and positioning of impacted canines on panoramic dental X-rays. Lastly, further research is required to refine the model for clinical implementation and to explore its integration into routine orthodontic practice
dc.identifier.citationÖzcan, M., Erdem, B., Turan, B., Tokatlı, N., Şar, Ç., & Özdemir, F. (2024). Deep-learning model for assessing difficulty in localizing impacted canines. International Dental Journal (FDI World Dental Congress), 74(Supplement 1), pp. S3-S3. https://doi.org/10.1016/j.identj.2024.07.578
dc.identifier.doi10.1016/j.identj.2024.07.578
dc.identifier.endpageS3
dc.identifier.issn1875-595X
dc.identifier.issn0020-6539
dc.identifier.issueSupplement 1
dc.identifier.scopusqualityQ1
dc.identifier.startpageS3
dc.identifier.urihttps://doi.org/10.1016/j.identj.2024.07.578
dc.identifier.urihttps://hdl.handle.net/20.500.13055/844
dc.identifier.volume74
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.indekslendigikaynak.otherSCI-E - Science Citation Index Expanded
dc.institutionauthorÖzcan, Mustafa
dc.institutionauthorErdem, Buket
dc.institutionauthorTokatlı, Nazlı
dc.institutionauthorŞar, Çağla
dc.institutionauthorÖzdemir, Fulya
dc.institutionauthorid0009-0004-6316-6072
dc.institutionauthorid0000-0001-9840-4211
dc.institutionauthorid0000-0003-4966-9779
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofInternational Dental Journal (FDI World Dental Congress)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep-Learning Model
dc.subjectLocalizing Impacted Canines
dc.subjectPanoramic X-Rays
dc.subjectConvolutional Neural Network (CNN)
dc.titleDeep-learning model for assessing difficulty in localizing impacted canines
dc.typeConference Object
dspace.entity.typePublication

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Tam Metin / Full Text
Boyut:
560.33 KB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Kapalı Erişim
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Ortodonti Ana Bilim Dalı Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu