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Three-dimensional digital technologies and artificial intelligence (AI) are one of the most recent and relevant advancements in the field of dentistry, including in orthodontics. These systems have enabled clinicians to increase the accuracy of diagnosis and treatment planning, to improve patient care along with reducing treatment planning time. The present study aims to explore possible applications of the artificial intelligence (AI) to examine the upper airway and to assess the reliability of the automatic evaluation of cone-beam computed tomography (CBCT). Thirty CBCTs, with the field of view 15*15, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic orthodontic report of each was generated as the basis of automatic evaluation. The same CBCTs were manually evaluated by independent evaluator. 3D color visualization of upper airway, total and minimum volume, UPW, LPW, PASmin and A/N ratio were assessed. The data were collected to allow statistical analysis with Microsoft Excel software. A total of 60 reports were created for 30 CBCTs. Reliability the correspondence between the two methods was 93.3%. A positive correlation between the minimum sagittal linear dimension (MSLD) on 2-dimensional reconstructed lateral cephalograms and minimum cross-sectional area (MCSA) on the CBCTs and a negative correlation between A/N ratio and total area airway were established. The AI system can be helpful as an initial evaluation of upper airway screening CBCTs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed. The AI technology can also help improve the accuracy of diagnoses for orthodontic treatment, therefore, helping orthodontists work more efficiently. However, the AI has shortcomings in assessing adenoid hypertrophy (AH). In conclusion, the fields of artificial intelligence in orthodontic diagnostics can be useful for upper airway screening and also have great priorities. Research in the field of upper airway diagnostics using artificial intelligence requires further studies. [ABSTRACT FROM AUTHOR]