Analysis of the correlation between thyroid hormones and thyroid volume by gender: A volumetric computed tomography study

The aim of this study was to evaluate the correlation between triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) hormones and thyroid gland volume with volumetric analysis performed by using computed tomography (CT) images. In this retrospective study, IV contrasted thoracic CT images taken for different indications between 2019 January and 2020 January were scanned from the archive system of the hospital. 67 (31F, 36M) individuals chosen randomly among patients whose CT results were reported as normal and who had taken thyroid hormone tests within the past week were included in the study. Images in Digital Imaging and Communications in Medicine format were transferred to the personal work station program (Horos Medical Image Viewer). By using the Region of Interest (ROI) console in the current program, a three dimensional model was obtained by drawing the border of the thyroid gland in sections varying between 15 and 25. Volume values of this three-dimensional model and TSH, T3, T4 values of the individuals were compared. While no correlation was found between thyroid gland volume and T3 and T4 hormones, a negative significant correlation was found with TSH. In terms of gender, thyroid gland volume, T3, T4 values were found to be statistically significantly higher in women when compared with men (p?0.05). TSH value was found to be higher in women when compared with men (p=0.005). No statistically significant difference was found in T4 value (p=0.057). Radio-anatomical volumetric data of the thyroid gland presented in this study and its correlation with thyroid functions will be beneficial to clinicians working in the field in both internal and surgical medicine branches and will also guide future studies

A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium

The 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

Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms

Introduction: 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

Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth

Abstract Background The 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. Results The 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. Conclusions Our 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