Determine The Age Range Based On Machine-Learning Methods From Skeletal Angles Of The Face (Glabella And Maxilla Angle And Length And Width Of Piriformis) In A CT Scan

One of the main steps in identifying a person in forensic medicine is determining age from skeletal remains, including the skull. This study aimed to investigate the possibility of predicting age from facial angles (glabella, piriformis, and maxillary angle and measuring peripheral length and width) with artificial intelligence in CT scans.

The study method is cross-sectional, using a questionnaire and simple random sampling.CT scan samples that can be accurately measured are selected. For exclusion criteria, gender uncertainty and the possibility of measurement based on the quality of the CT scan, the researchers examined the angles of the face (angle of the glabella and maxilla and length and width of the piriformis) for 100 men and 100 female. The mean, standard deviation of the age was 39.16 ± 2.22 years for men and 47.84 ± 2.46 years for women. The samples were classified based on age differences, and then the data were analyzed using machine learning algorithms to determine the age group.

After determining the exact amount of measurement, the data were evaluated by machine learning algorithms to determine the age group. Accordingly, in the age group classification based on the World Health Organization (with an age difference of 10 years)(years±5) with 100% accuracy and in the second classification (with an age difference of 5 years)(years±2.5) with 88% accuracy and 79% precision of the age group was predicted.

The obtained data show the importance of new artificial intelligence methods, including machine learning, in providing new methods for determining age groups (age±2.5) through skull angles with high accuracy in cases where even cranial remains are found in identification in forensic medicine.