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

Katarzyna Cieślińska (), Katarzyna Zaborowicz, Maciej Zaborowicz and Barbara Biedziak
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Katarzyna Cieślińska: Department of Orthodontics and Facial Abnormalities, University of Medical Sciences in Poznan, Colegium Maius, Fredry 10, 61-701 Poznan, Poland
Katarzyna Zaborowicz: Department of Orthodontics and Facial Abnormalities, University of Medical Sciences in Poznan, Colegium Maius, Fredry 10, 61-701 Poznan, Poland
Maciej Zaborowicz: Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, Poland
Barbara Biedziak: Department of Orthodontics and Facial Abnormalities, University of Medical Sciences in Poznan, Colegium Maius, Fredry 10, 61-701 Poznan, Poland

IJERPH, 2022, vol. 19, issue 22, 1-15

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.

Keywords: pantomographic radiograph (PR); artificial neural network (ANN); neural modeling; digital radiographs analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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