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    Deep-learning model for assessing difficulty in localizing impacted canines
    (Elsevier, 2024) Özcan, Mustafa; Erdem, Buket; Turan, Begüm; Tokatlı, Nazlı; Şar, Çağla; Özdemir, Fulya
    AIM 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
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    Does vertical pattern affect lip strain in open-bite patients?: A cephalometric study
    (Adıyaman University, 2024) Başal, Ece; Acar, Yasemin Bahar; Erdem, Buket
    Aim: To examine effect of skeletal pattern on lip strain in open-bite, in individuals with normal and increased vertical pattern. Materials and Methods: 56 open bite patients with Normovergent (NG) and Hyperdivergent (HG) vertical patterns (Mean age: 16.57 years) underwent cephalometric analysis. Soft tissue labial, hard tissue, and dental inclinations were measured. Statistical analyses were performed using Kolmogorov Smirnov, Mann Whitney-U, and independent sample t-tests; Pearson and Spearman correlation analyses; and Linear regression analysis. Results: In HG, each degree of SN-UOP increase caused 0.371 mm increase in lower lip strain. While in NG, upper lip strain was associated with IMPA and SNB (each degree caused 0.14 mm increase and 0.207 mm decrease respectively). Conclusion: IMPA, SN-UOP and SNB were found to be the determinants of lip strain. Dental, vertical, and sagittal variables showed association with lower face.
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    Ortodontide model ve fotoğraf analizi
    (Türkiye Klinikleri, 2023) Erdem, Buket; Şar, Çağla; Sayınsu, Korkmaz
    Ortodontide teşhis ve tedavi planlamasında modeller ve dental fotoğrafların önemi yadsınamaz. Bu yöntemler; dişler, kemikler ve yumuşak dokular hakkında kapsamlı bilgiler verebildiği gibi, aynı zamanda tüm kayıt yöntemleri arasında en noninvaziv yöntem olmaları yönünden de değerlidirler. Günümüzde üç boyutlu teknolojilerin gelişmesiyle birlikte her alanda olduğu gibi ortodontide de hem bu kayıt yöntemlerinde hem de analiz yöntemlerinde önemli gelişmeler yaşanmaktadır. Bu derlemenin amacı, günümüz ortodontisinde kullanılan diyagnoz yöntemlerinden model ve fotoğraf analiz yöntemlerini ve dijital teknolojideki gelişmelerin bu analizlere katkılarını incelemektir.
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    The impact of female orthodontists in 21st century research: A bibliometric analysis of the 1000 most cited articles (2000−2023)
    (W.B. Saunders, 2025) Şar, Çağla; Erdem, Buket; Eğlenen, Merve Nur; Koçak, Murat; Germeç-Çakan, Derya
    The involvement of female orthodontists in academic research has markedly increased in the 21st century. This bibliometric analysis evaluates the 1000 most-cited orthodontic articles to assess the influence of female ortho dontists in research between the years 2000−2023. A total of 677 publications featuring at least one female ortho dontic author were identified, with 54 % being first-written by women and 4.58 % produced only by female contributions. Forty-eight percent of these publications were published in the American Journal of Orthodontics and Dentofacial Orthopedics. The United States ranked first in publications, followed by Brazil and Italy, with the University of Michigan being the most productive institution. Research trends have transitioned from root resorp tion and skeletal growth in earlier years to artificial intelligence and digital technologies in more recent times.