Evaluation of the Second Premolar’s Bud Position Using Computer Image Analysis and Neural Modelling Methods

Abstract

Panoramic radiograph is a universally used diagnostic method in dentistry for identifying various dental anomalies and assessing developmental stages of the dentition. The second premolar is the tooth with the highest number of developmental abnormalities. The purpose of this study was to generate neural models for assessing the position of the bud of the second premolar tooth based on analysis of tooth–bone indicators of other teeth. The study material consisted of 300 digital pantomographic radiographs of children in their developmental period. The study group consisted of 165 boys and 135 girls. The study included radiographs of patients of Polish nationality, aged 6–10 years, without diagnosed systemic diseases and local disorders. The study resulted in a set of original indicators to accurately assess the development of the second premolar tooth using computer image analysis and neural modelling. Five neural networks were generated, whose test quality was between 68–91%. The network dedicated to all quadrants of the dentition showed the highest test quality at 91%. The training, validation and test subsets were divided in a standard 2:1;1 ratio into 150 training cases, 75 test cases and 75 validation cases.

On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging

“…Such difficulties may be overcome, in and efficient and reliable way, by the adoption of Artificial Intelligence (AI) strategies, suitable to mimic neural schemes and to reproduce the behaviour of complex structured systems. In the last decades, AI and, specifically, Artificial Neural Network (ANN) methodology have been successfully applied, with growing interest in various engineering and multidisciplinary research fields, such as structural engineering (see, e.g., [39][40][41][42]), biomedical engineering (see, e.g., [43][44][45]), agricultural engineering (see, e.g., [46][47][48][49]). In particular, in corrugated paperboard research and related applications, ANNs have been employed limited to calibration of mechanical constitutive parameters, [50] estimation of edge crush resistance, [51] evaluation of effects by hand and ventilation holes on box compressive strength.…”