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Öğe 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, SeymaIntroduction: 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.Öğe 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, ZulalObjective: 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.Öğe Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks(Pera Yayincilik Hizmetleri, 2023) Erkartal, Halil Saban; Tatli, Melike; Secgin, Yusuf; Toy, Seyma; Duman, Suayip BurakObjective: The aim of this study is to predict gender with parameters obtained from the upper dental arch by using machine learning algorithms (ML) machine learning algorithms and artificial neural networks to provide optimum aesthetics, functionality, long-term stability, diagnosis and treatment intervention in orthodontics, forensic medicine and anthropology. Methods: The study was conducted on cone-beam computed tomography (CBCT) images of 176 individuals between the ages of 18 and 55 who did not have any pathologies or surgical interventions in their upper dental arcade. The images obtained were transferred to RadiAnt DICOM Viewer program in Digital Imaging and Communications in Medicine format and all images were brought to orthogonal plane by applying 3D Curved Multiplanar Reconstruction. Length and curvature length measurements were performed on these images brought to orthogonal plane. The data obtained were used in ML algorithms and artificial neural networks input and rates of gender estimation were shown. Results: In the study, an accuracy ratio of 0.86 was found with ML models linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) algorithm and an accuracy ratio of 0.86 was found with random forest (RF) algorithm. It was found with SHAP analyser of RF algorithm that the parameter of width at the level of 3rd molar teeth contributed the most to gender. An accuracy rate of 0.92 was found as a result of training for 500 times with multi-layer classifier perceptron (MLCP), which is an artificial neural network (ANN) model. Conclusion: As a result of our study, it was found that the parameters obtained from the upper dental arcade showed a high accuracy in estimation of gender. In this context, we believe that the present study will make important contributions to forensic sciences.Öğe 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, ZuelalIntroduction: 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.Öğe Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms(Wolters Kluwer Medknow Publications, 2022) Secgin, Yusuf; Oner, Zulal; Turan, Muhammed Kamil; Oner, SerkanIntroduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.Öğe 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 OkanPurpose: 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.Öğe Sex and age estimation with corneal topography parameters by using machine learning algorithms and artificial neural networks(Int Assoc Law & Forensic Sciences, 2024) Yilmaz, Nesibe; Secgin, Yusuf; Mercan, KadirBackground The aim of this study, which was based on this hypothesis, was to estimate sex and age by using a machine learning algorithm (ML) and artificial neural networks (ANN) with parameters obtained from the eyeball. The study was conducted on corneal topography images of 155 women and 155 men aged between 6 and 87 who did not have surgical intervention or pathology in their eyeballs. In the study, the individuals were divided into four different age groups 6-17, 18-34, 35-55, and 56-87. Sex and age estimation was carried out by using the numerical data of parameters obtained as a result of corneal topography imaging in ML and ANN inputs.Results As a result of our study, in sex determination, a 0.98 accuracy rate (Acc) was obtained with the logistic regression algorithm, one of the ML algorithms, and 0.94 Acc was obtained with the MLCP model, one of the ANN algorithms; in age estimation, 0.84 Acc was obtained with RF algorithm, one of the ML algorithms. With the SHAP analyzer of the Random Forest algorithm, through which the effects of parameters on the overall result are evaluated, the parameter that made the highest contribution to sex estimation was found to be corneal volume, and the parameter that made the highest contribution to age estimation was found to be pupil Q parameter.Conclusion As a result of our study, it was found that parameters obtained from the eyeball showed a high accuracy in sex and age estimation.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, ZulalBackgroundThe 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.Öğe Sex estimation using sternum part lenghts by means of artificial neural networks(Elsevier Ireland Ltd, 2019) Oner, Zulal; Turan, Muhammed Kamil; Oner, Serkan; Secgin, Yusuf; Sahin, BunyaminIn addition to the pelvis, cranium and phalanges, the sternum is also used for postmortem sex identification. Bone measurements may be obtained on cadaveric bones. Alternatively, computerized tomography may be used to obtain measurements close to the original ones. Moreover, usage of artificial neural networks (ANNs) in the field of medicine has started to provide new horizons. In this study, we aimed to identify sex by an ANN using lengths of manubrium sterni (MSL), corpus sterni (CSL) and processus xiphoideus (XPL) and sternal angle (SA) from computerized tomography (CT) images brought to an orthogonal plane. This study used the thin-slice thoracic CT images of 422 cases (213 female, 209 male) with an age range of 27-60 years brought to the orthogonal plane. Measurements of MSL, CSL, XPL and SA were analyzed with a multilayer artificial neural network that used stochastic gradient descent (SGD) for optimization and two hidden layers. MSL, CSL and XPL were longer, and SA was wider in men (MSL p = 0.000, CSL p = 0.000, XPL p = 0.000, SA p = 0.02). In the case of the two hidden layers of the network with 20 and 14 neurons in the hidden layers, respectively, learning rate of 0.1 and momentum coefficient of 0.9, the accuracy (Acc) of sex prediction was 0.906. In order to define a more realistic performance of the network, bootstrap was run with the confidence interval of 94%. A sensitivity (Sen) value of 0.91 and a specificity (Spe) value of 0.90 were calculated. The success rates that were achieved in sex identification with measurements on the skeleton using ANN were observed to be higher than those achieved by linear models. Also, sometimes all parts of the bones may not be found or might be deformed. In this case, the number of parameters used for the estimation will be incomplete. The ANN has the strong advantage to be able to estimate despite the missing parameter. (C) 2019 Elsevier B.V. All rights reserved.Öğe A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium(Nature Portfolio, 2022) Toy, Seyma; Secgin, Yusuf; Oner, Zulal; Turan, Muhammed Kamil; Oner, Serkan; Senol, DenizThe aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p <= 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy.Öğe A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals(Pergamon-Elsevier Science Ltd, 2019) Turan, Muhammed Kamil; Oner, Zulal; Secgin, Yusuf; Oner, SerkanBackground: Predicting sex is an important problem in forensic medicine. The femur, patella, mandible and calcaneus bones are frequently used in predicting sex. In our study, we aimed to use the artificial neural network (ANN) technique to predict sex by measuring the values of the phalanges of the first and fifth toes and the first and fifth metatarsal bones. Method: All bone measurements were conducted on the direct X-ray images of 176 males and 178 females in the age range of 24-60 years. The multilayer perceptron classifier (MLPC) input layer included parameters on the bone length measurements of phalanx proximalis I, phalanx distalis I, metatarsal I, phalanx proximalis V, phalanx medialis V, phalanx distalis V and metatarsal V. The output layer contained two neurons to define the male and female sexes. The present study used an MLPC model that had two hidden layers, and the first and second hidden layers contained 14 and 7 nodes, respectively. Results: The model had an overall accuracy (Acc) of 0.95, specificity (Spe) of 0.97, sensitivity (Sen) of 0.95 and Matthews correlation coefficient (Mcc) of 0.92. While the sex prediction success of our proposed model was higher in women, the results were more specific in men and more sensitive in women (Acc(male) = 0.93, Acc(Female) = 0.98, Sen(male) = 0.93, Spe(male) = 0.98, Sen(Female) = 0.98 and Spe(Female) = 0.93). Conclusions: This study demonstrated that the ANN model for length measurements on small bones is a highly effective instrument for sex prediction.
