Deep bite is one of the most common dental anomalies in children and adolescents. Timely and accurate diagnosis of this pathology is crucial for selecting the optimal orthodontic treatment strategy and preventing the development of functional and morphological disorders. The aim of this study is to develop and clinically test an algorithm for diagnosing deep bite in children using artificial intelligence technologies. The study utilized clinical, anthropometric, photometric, and radiographic examination methods, as well as computer analysis of teleradiographs and digital dental models. The proposed AI algorithm automated the analysis of diagnostic data, increased the accuracy of deep bite determination, and optimized orthodontic treatment planning. The use of intelligent diagnostic systems helps reduce inter-clinical variability and improve the effectiveness of orthodontic care for children.

Deep bite, children, orthodontics, artificial intelligence, diagnostics, teleradiography, AI algorithm.

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