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

Abstract

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.

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

“…When the literature is reviewed, it can be seen that hip bone and cranial bones are the first preferred bones in sex determination. This is because some parts of the pelvis and cranial bones show sex specific morphological differences [11,15,38]. In cases when these bones are not present, it can be seen that femur [9], tibia and fibula [14], patella [32], sternum [33], humerus [22], ulna and radius [19], foot and hand bones [41] have also been tried in sex prediction.…”

Estrous Cycle and Egg Release Resets with Light Entrainment Inspired by Neurodynamics in the Circadian Pacemaker

“…One bene t of bulk ber photometry measurement is that it was su ciently powered for a machine learning (ML) algorithm to predict sex and estrous state based on power-spectral analysis of neuronal activity. ML is currently used to identify sex in biological samples or behaviors [50][51][52][53] , and, while ML has been used to identify estrous cycle stages in vaginal smears [54][55][56] , this is, to our knowledge, its rst use to identify estrous cycle states based on neuronal activity.…”

Current Evidence on Bioinformatics Role and Digital Forensics That Contribute to Forensic Science: Upcoming Threat

“…Forensic photography is central to the digital approach because photographic files are stored in hardware, so they must be digital files. Forensic photography used in medical approaches can provide scientific evidence such as digital dental models [49], dental photography [50], skeletal radiography [51], X-ray CT fossil [52], and CT images of human cranium [53].…”

“…In the case of image processing from digital forensic photography files, the contribution of artificial intelligence such as machine learning (ML) is essential to aid the identification process [54]. With the ML approach, some cases such as sex determination [52] and forensic entomology [53] can be revealed. Besides, all digital photography files must be stored privately for privacy [50].…”