نتیجه جستجو - Long bones

ردیف | عنوان | نوع |
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1 |
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 |
مقاله انگلیسی |

2 |
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) |
مقاله انگلیسی |