Background: One of the main steps in identifying a person in forensic medicine is determining the age of 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 a CT scan. Methods: The cross-sectional study method is simple random sampling using a questionnaire. Accurately measurable CT scan samples are selected. For exclusion criteria, gender uncertainty, and the possibility of measurement based on CT scan quality, the researchers examined the facial angles (angle of the glabella and maxilla and length and width of the piriformis) for 100 men and 100 women. The Mean±SD 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. Results: 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 (WHO) (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. Conclusion: The obtained data show the importance of new artificial intelligence methods, including machine learning, in providing new methods to determine age groups (age±2.5) through skull angles with high accuracy in cases where even cranial remains are found in identification in forensic medicine.

Background: This study aimed to determine the normal dimensions of the mitral annulus (MA) in Iranian population. Materials and Methods: This cross-sectional study was conducted using 88 fresh hearts of male and female cadavers for six months in Mashhad, Iran. Normal data were determined by measuring the exact dimensions of the MA in fresh hearts of patients who had died of non-cardiac causes and considering some parameters such as age, gender, stature, and weight. Images of the valves and leaflets were prepared by marking the anterior (A2, midpoint of anterior) and posterior areas of P1, P2, and P3 using a needle. To analyze the data, SPSS version 16 was used. Results: The means of anatomic area, anatomic perimeter, inter-commissural distance, A2-P1, A2-P2, A2-P3, Base-P1, Base-P2, Base-P3, and Base-A were 14±1.28, 8.3±1, 2.7±0.42, 2.27±0.37, 2.3±0.43, 2.06±0.35, 1.66±0.43, 1.2±0.97, 1.5±0.66, and 3.2±0.52, respectively. Comparison of the age groups regarding valve leaflets showed that Strut-P1 and Base-P2 were significantly different. Comparison of the valve leaflets and sub-valve indicators between the two genders reflected no significant differences. Age groups differed significantly in terms of Strut-P1 and Base-P2 (P=0.004 and P=00.1, respectively). Conclusions: A2-P3, A2-P1, anatomic perimeter, and anatomic area were found to be related to gender. A2-P1 and A2-P2 and some leaflet indicators such as Strut-P1 and Base-P2 were associated with age, whereas Base-P2 was affected by body mass index. [GMJ.2018;7:e1078]

In light of the results of the present study, undertaken on fresh cadaveric human heart specimens, the current segmental prosthetic system for 3D remodelling of the tricuspid valve annulus seems to be safe vis-à-vis the anatomical integrity of the RCA. Further in vivo studies are needed to investigate the functional features of the current prosthetic system with a view to addressing the complex pathophysiology of secondary tricuspid valve regurgitation.