Metrical age assessment using image analysis and artificial neural networks

Computer imaging methods are widely used in medical related problems. Imaging is readily used for diagnostic purposes due to its availability, non-invasiveness, and high quality. Due to the great number of medical conditions, as well as due to the frequent lack of qualified medical staff, there has been a need to automate the evaluation of radiological examinations. Therefore, a quickly growing branch of science is the neural analysis of medical images. This paper presents the possibility of using computer image analysis and neural modeling methods in the assessment of metric age of children and adolescents from digital pantomographic images. The analog methods used in the clinical assessment of the patient’s chronological age are subjective and characterized by low accuracy. The paper presents the possibility of using RBF networks and deep learning in the assessment of the metric age of children aged from 4 to 15 years. As a result, two neural models with quality ranging from 97 to 99% were obtained.