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  • Küçük Resim
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    Cone-beam computed tomography evaluation of C-shape canals and longitudinal grooves of mandibular first and second molar teeth
    (2019) Duman, Burak Suayip; Duman, Sacide; Bayrakdar, Ibrahim Sevki; Yasa, Yasin; Gumussoy, Ismail
    Aim: This study aims to evaluate the anatomical features and prevalence of C-shaped roots and longitudinal grooves in mandibular first and second permanent teeth using cone-beam computed tomography (CBCT). Material and Methods: CBCT records of first and second mandibular teeth from 839 patients who applied to the Department of Oral and Maxillofacial Radiology between 2011 and 2018 were used. The CBCT examination was performed at five different axial levels and the mandibular molars were classified as types of longitudinal groove and C-shape according to the Fan criteria. Differences between genders, age groups, left and right side and type of tooth were determined. Result: A total of 2903 teeth (1321 first molars and 1582 second molar) from 839 patients were included in the study. C-shaped canals were found in mandibular first molar teeth with a prevalence of 0.15%, while 4.1% in mandibular second molar teeth. Only two mandibular first molars exhibited C-shaped canal. Female patients had a higher prevalence than males. Longitudinal grooves were most commonly found lingual surface. Conclusions: The occurence of C-shaped canals in mandibular first and second molars among Turkish population was generally less than in other populations. CBCT is a valuable tool to evaluate the C-shaped root canal configuration in vivo.
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    Öğe
    Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth
    (Int Assoc Law & Forensic Sciences, 2023) Senol, Deniz; Secgin, Yusuf; Duman, Burak Suayip; Toy, Seyma; Oner, Zulal
    BackgroundThe aim of this study is to obtain a highly accurate and objective sex and age estimation by using the parameters of maxillary molar and canine teeth obtained from cone beam computed tomography images in the input of machine learning algorithms. Cone beam computed tomography images of 240 people aged between 25 and 54 were randomly selected from the archive systems of the hospital and transferred to Horos Medikal. 3D curved multiplanar reconstruction was applied to these images and a 3D image was obtained. The resulting image was brought to the orthogonal plane and the measurements were made by superimposing them.ResultsThe results were grouped in four different age groups (25-30, 31-36, 37-49, 50-54) and recorded. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation with ADA Boost Classifier algorithm, while in age estimation, the highest accuracy rate was found as 0.84 between 25-30 and 31-36 age groups with random forest algorithm, as 0.74 between 25-30 and 37-49 age groups with random forest and ADA Boost Classifier algorithms and as 0.85 between 25-30 and 50-54 age groups with random forest algorithm.ConclusionsOur study differs from other studies in two aspects; the first is the selection of a sensitive method such as cone beam computed tomography, and the second is the selection of machine learning algorithms. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation and as 0.85 in age estimation with parameters of maxillary canine and molar teeth.