Gender Estimation from Morphometric Measurements of Mandibular Lingula by Using Machine Learning Algorithms and Artificial Neural Networks.

Background: Sex determination from the bones is of great importance for forensic medicine and anthropology. The mandible is highly valued because it is the strongest, largest and most dimorphic bone in the skull.

Aim: Our aim in this study is gender estimation with morphometric measurements taken from mandibular lingula, an important structure on the mandible, by using machine learning algorithms and artificial neural networks.

Investigation of the effectiveness of atmospheric pressure cold plasma on sciatic nerve injury in rats.

Background: The aim of this study was to evaluate the efficacy of atmospheric pressure cold plasma jet and plasma activated medium (PAM) on sciatic nerve injury (SNI).

Materials And Methods: Rats were divided into 6 groups (n = 10); group 1 (Sham), group 2 (SNI), group 3 (SNI + Atmospheric pressure cold plasma jet 5 min), group 4 (SNI + Atmospheric pressure cold plasma jet 10 min), group 5 (SNI + PAM 5 min), group 6 (SNI + PAM 10 min). On the 1st, 8th, 15th, 22nd days of the study, atmospheric pressure cold plasma jet was applied to rats in groups 3 and 4, and PAM was applied to rats in groups 5 and 6.

Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus.

Background: Determination of bone age is a critical issue for forensics, surgery, and basic sciences.

Aim: This study aims to estimate age with high accuracy and precision using Machine Learning (ML) algorithms with parameters obtained from calcaneus x-ray images of healthy individuals.

Method: The study was carried out by retrospectively examining the foot X-ray images of 341 people aged 18-65 years.

Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms.

Background: The aim of this study is to predict sex with machine learning (ML) algorithms by making morphometric measurements on radiological images of the first and fifth metatarsal and phalanx bones.

Materials And Methods: In this study, radiologic images of 263 individuals (135 female, 128 male) between the ages of 27 and 60 were analysed retrospectively. The images in digital imaging and communications in medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer programme.

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.