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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: Traffic accidents are one of the most common causes of mortality and physical disabilities, endangering the lives of many people all over the world annually and are among the top public health problems worldwide. In the present study, we aimed to investigate the trend of mortality rate due to traffic accidents in the provinces of Iran.Methods: In this cross-sectional study, all the deaths caused by traffic accidents in Iran during 2006-2018 were investigated. Using the population of the country by age, sex, and provinces of the country, the mortality rate was calculated and the trend of 13-year changes was studied. The negative binomial regression was used to analyze the linear or nonlinear trend of reduction in mortality rate during the study years. Microsoft Excel 2016 and Stata version 14 software were used to analyze the data.Results: During the study period, 259995 traffic accidents deaths occurred in Iran, of which 78.6% were men and 21.4% were women. The mean age of the deceased was 37.6 ± 20.7 years (37.4 ± 20 years in men and 38.6 ± 23 years in women). The number of the deaths in these years has decreased from 27,567 in 2006 to 17,183 in 2018 and the mortality rate has dropped from 39 per 100,000 in 2006 to 21 per 100,000 in 2018.Conclusion: Despite the decreasing trend in the mortality rate of traffic accidents in Iran during the study years, this trend was different across the provinces. Therefore, it seems necessary to design epidemiological studies to be conducted in different area and provinces of a country, to better and more accurately determine the factors affecting the occurrence of these deaths.

Objective: Various non-surgical treatments are used to treat Carpal tunnel syndrome, including hand therapy. In this study, the effect of Fateh Iranian hand therapy on this disease has been investigated for the first time. Method: In this controlled clinical trial, 58 female patients (78 hands) eligible for carpal tunnel syndrome were divided into two groups of intervention (splint, Fateh hand therapy, and exercise) and the control group (splint only). Each person in the intervention group received about 7 minutes of soft tissue manipulation for 6 sessions and performed two active exercises at home daily. Symptom severity and functional capacity were assessed with the Boston questionnaire, pain intensity, and electrodiagnostic findings at the beginning and tenth week, and patient satisfaction in the tenth week of the experiment. Results: Data of 51 patients, all-female (68 hands), were analyzed. The age distribution was the same in both groups. In the intervention group, we saw a significant improvement in symptom severity and functional capacity compared to the control group (P-value<0.05). In addition, pain changes in the intervention group were significantly more than in the control group (P-value<0.05). The values ​​of electrodiagnostic variables at the beginning and end of the design were not significantly different between the two groups (P-value>0.05). Comparison of changes in these values ​​did not show a significant difference between the two groups (P-value>0.05). Satisfaction in the intervention group was significantly higher (P-value<0.05). Conclusion: Fateh method is effective in reducing the symptoms of mild to moderate carpal tunnel syndrome.