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    Analysis of the effects of total pneumatized turbinate volume on septum deviation, maxillary sinus volume, and maxillopalatal parameters: A multidetector computerized tomography study
    (Wolters Kluwer Medknow Publications, 2023) Senol, Deniz; Oner, Serkan; Secgin, Yusuf; Oner, Zulal; Toy, Seyma
    Introduction: The aim of this study was to examine the effects of pneumatized turbinate volume (PTV) on nasal septum deviation (NSD), maxillary sinus volume (MSV), and maxillopalatal parameters with multidetector computed tomography (MDCT). Material and Methods: MDCT images of a total of 73 patients (35 females and 38 males) between the ages of 25 and 58 years were used in the study. PTV, MSV, and NSD angle and direction and interalveolar distance (IAD), maxillary spin distance (MSD), and maxillopalatal angle (MPA) measurements were made on images brought to the orthogonal plane in 3 plans. Results: Turbinate pneumatization (superior, middle, or inferior) was found in a total of 55 (75.3%) patients (28 females and 27 males). The number of patients with turbinate pneumatization on the right side was 14 (19.2%), while the number of patients with turbinate pneumatization on the left side was 15 (20.5%) and the number of bilateral pneumatization was 26 (35.6%). While no significant association was found between the presence of turbinate pneumatization and septal deviation angle, MSV, MPA, IAD, and MSD measurements, a significant difference was found between the groups in terms of PTV (P < 0.05). No significant association was found between NSD direction and all parameters. Discussion and Conclusion: In this study, we conducted with MDCT images, in addition to the highest incidence in turbinate pneumatization with 75.3%; it was found that PTV did not have an effect on NSD amount, MSV, and maxillopalatal parameters. Men were found to have higher NSD angle, higher maxillary sinus aeration, and larger IAD when compared with women.
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    Can Typical Cervical Vertebrae Be Distinguished from One Another by Using Machine Learning Algorithms? Radioanatomic New Markers
    (Duzce Univ, Fac Medicine, 2023) Senol, Deniz; Secgin, Yusuf; Toy, Seyma; Oner, Serkan; Oner, Zulal
    Objective: The aim of this study is to distinguish the typical cervical vertebrae that cannot be separated from one another with the naked eye by using machine algorithms (ML) with measurements made on computerized tomography (CT) images and to show the differences of these vertebrae.Methods: This study was conducted by examining the 536 typical cervical vertebrae CT images of 134 (between the ages of 20 and 55) individuals. Measurements of cervical vertebrae were made on coronal, axial and sagittal section. 6 different combinations (Group 1: C3 - C4, Group 2: C3 - C5, Group 3: C3 - C6, Group 4: C4 - C5, Group 5: C4 - C6, Group 6: C5 - C6) were formed with parameters of each vertebrae and they were analyzed in ML algorithms. Accuracy (Acc), Matthews correlation coefficient (Mcc), Specificity (Spe), Sensitivity (Sen) values were obtained as a result of the analysis.Results: As a result of this study, the highest success was obtained with Linear Discriminant Analysis (LDA) and Logistic Regression (LR) algorithms. The highest Acc rate was found as 0.94 with LDA and LR algorithm in Groups 3 and Group 4, the highest Spe value was found as 0.95 with LDA and LR algorithm in Group 5, the highest Mcc value was found as 0.90 with LDA and LR algorithm in Group 5 and the highest Sen value was found as 0.94 with LDA and LR algorithm in Groups 3 and 5. Conclusions: As a conclusion, it was found that typical cervical vertebrae can be distinguished from each other with high accuracy by using ML algorithms.
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    Gender Prediction Using Cone-Beam Computed Tomography Measurements from Foramen Incisivum: Application of Machine Learning Algorithms and Artificial Neural Networks
    (Wolters Kluwer Medknow Publications, 2024) Senol, Deniz; Secgin, Yusuf; Harmandaoglu, Oguzhan; Kaya, Seren; Duman, Suayip Burak; Oner, Zuelal
    Introduction: This study aims to predict gender using parameters obtained from images of the foramen (for.) incisivum through cone-beam computed tomography (CBCT) and employing machine learning (ML) algorithms and artificial neural networks (ANN).Materials and Methods: This study was conducted on 162 individuals in total. Precise measurements were meticulously extracted, extending from the foramen incisivum to the arcus alveolaris maxillaris, through employment of CBCT. The ML and ANN models were meticulously devised, allocating 20% for rigorous testing and 80% for comprehensive training.Results: All parameters that are evaluated, except for the angle between foramen palatinum majus and foramen incisivum-spina nasalis posterior (GPFIFPNS-A), exhibited a significant gender difference. ANN and among the ML algorithms, logistic regression (LR), linear discriminant analysis (LDA), and random rorest (RF) demonstrated the highest accuracy (Acc) rate of 0.82. The Acc rates for other algorithms ranged from 0.76 to 0.79. In the models with the highest Acc rates, 14 out of 17 male individuals and 13 out of 16 female individuals in the test set were correctly predicted.Conclusion: LR, LDA, RF, and ANN yielded high gender prediction rates for the measured parameters, while decision tree, extra tree classifier, Gaussian Naive Bayes, quadratic discriminant analysis, and K-nearest neighbors algorithm methods provided lower predictions. We believe that the evaluation of measurements extending from foramen incisivum to arcus alveolaris maxillaris through CBCT scanning proves to be a valuable method in gender prediction.
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    Morphometric examination of the hepatobiliary duct system in healthy individuals and patients with cholelithiasis: A radio-anatomic magnetic resonance cholangiopancreatography study
    (Cukurova Univ, Fac Medicine, 2023) Toy, Seyma; Senol, Deniz; Ciftci, Rukiye; Sevgi, Serkan; Secgin, Yusuf; Yildirim, Ismail Okan
    Purpose: Cholelithiasis is a common gallbladder disease with high morbidity and treatment cost. Although the disease has many formation factors such as bile duct obstruction, congenital anomalies, genetic and metabolic diseases, the main cause is gallstones. The aim of this study is to examine the radio-anatomic and demographic characteristics of the bile ducts of patients who have cholelithiasis due to gallstones by using magnetic resonance cholangiopancreatography (MRCP) and to compare with healthy individuals.Materials and Methods: The study was carried out by retrospectively scanning the MRCP images of 113 patients diagnosed with cholelithiasis and 87 healthy individuals who were referred to the hospital for various indications and had no gallbladder pathology. Results: According to the Spearman rho correlation test performed by ignoring gender, a significant correlation was found between right hepatic duct diameter (RHD-D) and right hepatic duct - cystic duct angle (RHDCD-A), and between left hepatic duct diameter (LHD-D) and common bile duct diameter (CBD-D). In the correlation analysis performed only among males, a significant correlation was found between RHDCD-A and right hepatic duct - left hepatic duct angle (RLHD-A), RHDCD-A and common hepatic duct diameter (CHD-D) parameters. In the correlation analysis performed only among women, a significant relationship was found between age and RHD-D, LHD-D, CHD-D, CBD-D, between RHDCD-A and cystic duct - gallbladder angle (CDG-A), RHD-D, and between CHD-D and cystic duct diameter (CD-D).Conclusion: This study will contribute to literature by revealing the morphometric characteristics and radio -anatomic information of the hepatobiliary systems of both patients with cholelithiasis and healthy individuals.
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    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.