[Review of age assessment methods of children and adolescents by teeth X-rays]. / Obzor metodov otsenki vozrasta detei i podrostkov po rentgenogrammam zubov.

Interest in the topic of age assessment for forensic medical identification of personality has not decreased for over the past decade. Establishing an exact age have a critical importance for law enforcement authorities, for example in case of wrongdoing by illegal migrants without identity documents. The search and systemic analysis of published researches devoted to age assessment by dental status in children and adolescents with subsequent updating of the directions of development in this scientific subject theme and the possibility of their realization in practice in the Russian Federation were carried out in order to have an objective concept of used methods of dental status assessment in the world practice.

[Age assessment using CT of knee joint and neural network technologies]. / Otsenka vozrasta s ispol'zovaniem KT kolennogo sustava i neirosetevykh tekhnologii.

Age assessment of living persons plays an important role in clinical and sports medicine, as well as in law practice. Traditional methods have a number of problems: age restrictions, technical difficulties of visualization, low reproducibility and subjectivity of estimation. The proposed approach, which implies the use of multispiral computed tomography (MSCT) and database mining, will eliminate these drawbacks and improve the estimation of age. The aim of the study was to investigate the use of deep learning algorithms to classify the age groups (with a threshold level of 18 years) for CT images of knee joint. The study included 455 MSCT images of the knee joint of male and female subjects aged from 13 to 24. The method included score assessment of the distal femur's epiphyseal synostosis stages, tibia and fibula proximal epiphyses and a preliminary statistical analysis of correlations between age and stages of synostosis. The challenge of binary classification of target age groups with the use of convolutional neural networks was implemented at the second phase of the trial. Various architectures of convolutional neural networks and their ensembles were tested. The result of the study showed that the total score of epiphyseal synostosis has the highest correlation with the age (r=0.88). The proposed method of chronological age assessment on the basis of the knee area CT images research using deep learning algorithms demonstrated a good result. The classification accuracy (threshold level of 18 years) was 0.86.

[Historical aspects of the problem of work capacity evaluation].

This article addresses one of the as yet unresolved issues pertaining to forensic medical examination of certain health problems, namely the absence of an accurate universally accepted approach to the interpretation of the term "working capacity". Historical prerequisites for the evolvement of such notions as "economic criterion" and "disability percentage tables" are discussed with special reference to the estimation of the degree of harm to human health. Analysis of the literature data is used to compare different points of view of the problem and ascertain their validity.