The success of treatment in orthodontics and maxillofacial surgery is directly dependent on accurate diagnosis and meticulous planning. Cephalometric analysis of teleroentgenography (TRG) data serves as the fundamental basis for this process, enabling the assessment of complex interrelationships of facial and skeletal structures. However, traditional manual tracing methods are not only time-consuming but are also susceptible to subjective errors contingent on the operator's experience. These inaccuracies can, in turn, lead to an incorrect treatment plan, a failure to achieve expected outcomes, and even negative consequences for the patient's health. The rapid development of digital technologies, particularly automated analysis systems based on artificial intelligence (AI), is revolutionizing gnatometric analysis. These systems have the potential to drastically increase the accuracy, speed, and, most importantly, the reproducibility of the analysis, thereby standardizing the diagnostic process and minimizing human-factor-related errors. Optimizing and widely implementing these modern methods into clinical practice not only improves diagnostic quality but also allows for more reliable prediction of treatment outcomes, virtual modeling of surgical interventions, and enhancement of the educational process. Therefore, the in-depth study and refinement of these technologies represent one of the most urgent tasks in modern dentistry.

teleroentgenography, cephalometric analysis, gnatometric analysis, orthodontics, digital dentistry, artificial intelligence, automated landmark identification.

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