Image analysis has an increasing role in the identification of individuals in forensic application. Beside the bones, microstructural of arteries can be used in age estimation study. Aorta is the largest elastic artery which consists of many elastic fibers. Elastin in arterial wall highly resist to chemical and physical influence. The purposes of the study were to quantify elastic fibers in tunica media in each location of the aorta and examine the correlation between elastic fibers and age by using image analysis program. A total of 36 human aortas were dissected in 4 locations. The aortas were obtained from cadavers with an age range of 20 to 90 years. Specimens were stained with Elastic Van Gieson staining. Histological images were investigated about elastic fibers using light microscope with cellSens program and aorta image analysis was used for the evaluation of data. The results showed that the mean percentage density of elastic fibers in the ascending aorta and the aortic arch increased. However, the mean percentage density of elastic fibers decreased in the 31 to 40 years age group in the thoracic aorta and the abdominal aorta and decreased in each location of aorta continuously until 81 to 90 years. The abdominal aorta showed the highest correlation with age (r=0.732) followed by the thoracic aorta, the aortic arch and the ascending aorta, respectively. Changes in the percentage density of elastic fibers in the tunica media of the aortic wall can be used to add information to age estimation for identification purposes.

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.