Computed tomographic analysis of medial clavicular epiphyseal fusion for age estimation in Indian population
تجزیه و تحلیل توموگرافی محاسبه شده از همجوشی اپیفیز داخلی لخته برای تخمین سن در جمعیت هند-2020
Forensic age estimation is a crucial aspect of the identification process. While epiphyseal fusion of long bones has been studied for age estimation since a long time, over the past few years, the role of medial clavicular epiphyseal fusion in age estimation is being explored. The medial clavicular epiphyseal fusion can be used to estimate age in young adults, and can also determine whether medicolegally significant ages of 16 and 18 years have been attained by an individual. The present study aimed at generating regression models to estimate age by evaluating the medial clavicular epiphyseal fusion in Indian population using Schmeling et al. and Kellinghaus et al. method, and to assess whether an individual’s age is over medicolegally significant thresholds of 16 and 18 years. Degree of ossification of the medial clavicular epiphysis was studied in CT images of 350 individuals aged 10.01–35.47 years. Significant statistical correlation (P < 0.001) was observed between the degree of fusion and the chronological age of the participants, with Spearman’s correlation (ρ) = 0.918 in females, and ρ = 0.905 in males. Regression models were generated using degree of ossification of medial end of clavicle of 350 individuals (147 females and 203 males) and these models were applied on a test set of 50 individuals (25 females and 25 males). Mean absolute error of 1.50 for females, 1.14 for males, and 1.32 for the total test set was observed when the variance between the chronological ages and estimated ages was calculated.
Keywords: Forensic age estimation | Forensic anthropology | Medial clavicular epiphyseal fusion | Computed tomography | Forensic radiology | Identification
Prediction of displacement in the equine third metacarpal bone using a neural network prediction algorithm
پیش بینی جابجایی در استخوان metacarpal سوم اسب با استفاده از الگوریتم پیش بینی شبکه عصبی-2019
Bone is a nonlinear, inhomogeneous and anisotropic material. To predict the behavior of bones expert systems are employed to reduce the computational cost and to enhance the accuracy of simulations. In this study, an artificial neural network (ANN) was used for the prediction of displacement in long bones followed by ex-vivo experiments. Three hydrated third metacarpal bones (MC3) from 3 thoroughbred horses were used in the experiments. A set of strain gauges were distributed around the midshaft of the bones. These bones were then loaded in compression in an MTS machine. The recordings of strains, load, Load exposure time, and displacement were used as ANN input parameters. The ANN which was trained using 3,250 experimental data points from two bones predicted the displacement of the third bone (R2 ≥ 0.98). It was suggested that the ANN should be trained using noisy data points. The proposed modification in the training algorithm makes the ANN very robust against noisy inputs measurements. The performance of the ANN was evaluated in response to changes in the number of input data points and then by assuming a lack of strain data. A finite element analysis (FEA) was conducted to replicate one cycle of force-displacement experimental data (to gain the same accuracy produced by the ANN). The comparison of FEA and ANN displacement predictions indicates that the ANN produced a satisfactory outcome within a couple of seconds, while FEA required more than 160 times as long to solve the same model (CPU time: 5 h and 30 min).
Keywords: Artificial neural network (ANN) | Displacement prediction | Finite element analysis (FEA) | Expert system | Long bones | Equine third metacarpal bone (MC3)