Assessment of deep learning technique for fully automated mandibular segmentation

Basit Öğe Kaydı
dc.authoridDuran, Gökhan Serhat / 0000-0001-6152-6178
dc.contributor.authorYurdakurban, Ebru
dc.contributor.authorSüküt, Yağızalp
dc.contributor.authorDuran, Gökhan Serhat
dc.date.accessioned2025-05-29T02:58:02Z
dc.date.available2025-05-29T02:58:02Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIntroduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible. Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model. To assess the model's performance, 15 cone-beam computed tomography scans from the test group were inputted into the model. The ground truth was obtained from manual segmentation data. Metrics including the Dice similarity coefficient, Hausdorff 95%, precision, recall, and segmentation times were calculated. In addition, surface deviations and volumetric differences between the automated and manual segmentation results were analyzed. Results: The automated model showed a high level of similarity to the manual segmentation results, with a mean Dice similarity coefficient of 0.926 +/- 0.014. The Hausdorff distance was 1.358 +/- 0.466 mm, whereas the mean recall and precision values were 0.941 +/- 0.028 and 0.941 +/- 0.022, respectively. There were no statistically significant differences in the arithmetic mean of the surface deviation for the entire mandible and 11 different anatomic regions. In terms of volumetric comparisons, the difference between the 2 groups was 1.62 mm3, which was not statistically significant. Conclusions: The automated model was found to be suitable for clinical use, demonstrating a high degree of agreement with the reference manual method. Clinicians can use open-source software to develop custom automated segmentation models tailored to their specific needs. (Am J Orthod Dentofacial Orthop 2025;167:242-9)
dc.identifier.doi10.1016/j.ajodo.2024.09.006
dc.identifier.endpage249
dc.identifier.issn0889-5406
dc.identifier.issn1097-6752
dc.identifier.issue2
dc.identifier.pmid39863342
dc.identifier.scopus2-s2.0-85215591205
dc.identifier.scopusqualityQ1
dc.identifier.startpage242
dc.identifier.urihttps://doi.org/10.1016/j.ajodo.2024.09.006
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30249
dc.identifier.volume167
dc.identifier.wosWOS:001409653800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMosby-Elsevier
dc.relation.ispartofAmerican Journal of Orthodontics and Dentofacial Orthopedics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250529
dc.titleAssessment of deep learning technique for fully automated mandibular segmentation
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Gokhan Serhat Duran_Makale.pdf
Boyut:
481.65 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Ortodonti Bölümü Yayın Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu