Deep bite is one of the most common dentofacial 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 was to develop and clinically evaluate an algorithm for diagnosing deep bite in children using artificial intelligence technologies. Clinical, anthropometric, photometric, and radiological examination methods were used in the study, along with computer analysis of lateral cephalograms and digital dental models.

The proposed AI algorithm made it possible to automate the analysis of diagnostic data, improve the accuracy of determining the type of deep bite, and optimize orthodontic treatment planning. The use of intelligent diagnostic systems contributes to reducing clinician variability and improving the effectiveness of orthodontic care for children.

Deep bite, children, orthodontics, artificial intelligence, diagnostics, cephalometric radiography, AI algorithm.

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