Age-at-death estimation based on radiological and image analysis methods in clavicle in a current Spanish population

Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study

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.

Forensic age estimation based on the pigmentation in the costal cartilage from human mortal remains

Age estimation is considered a crucial and challenging issue in forensic casework. Costal cartilage appears a potential mortal remain in age-at-death estimation attributable to its correlative alteration in color based on pigment accumulation with the advancing age. In this study, samples from the second costal cartilage were collected in a Chinese Han population, and the cross sections were subsequently scanned and digitalized in a standard way. Color change was quantified using mean gray value (MGV), which was measured by Photoshop CS5. After the exclusion of samples with factors which could impair the quality of images and the accuracy of values, a high correlation was demonstrated between age and MGV in samples. A linear regression model (AGE = 173.425-0.755*aveMGV) was established for age prediction, with its performance evaluated using both samples from the training set and the blind test set, in which a mean absolute deviation of 4.42 years and 3.57 years was obtained, respectively. Altogether, MGV could be reckoned as a precise quantification of pigmentation in costal cartilage and an excellent indicator of age prediction in the age interval from 20 to 60 years. Moreover, our strategy appears more user-friendly and accurate, thus exceedingly practical for age estimation in forensic anthropology.