Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth
Deniz Şenol, Yusuf Seçgin, Şuayip Burak Duman, Şeyma Toy, Zülal Öner
Abstract
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.