Identification of the main risk factors for the development of disorders of occlusive relationships.

Introduction: Violations of occlusive relationships are an urgent problem in dental practice due to the high prevalence, complexity of diagnosis and treatment, as well as the connection with common somatic diseases. These pathologies reduce the quality of life of patients and complicate the process of medical rehabilitation. Prevention aimed at early detection of risk factors is especially important. The purpose of this study is to identify the main risk factors for the development of disorders of occlusive relationships. Materials and methods: A randomized controlled trial was conducted with 120 patients aged 18 to 44 years. The study did not include patients with acute infectious diseases, decompensated chronic diseases, oncology, mental disorders and periodontal inflammation. The assessment included clinical examination, cone beam computed tomography (CBCT), electromyography (EMG), ultrasound examination of the temporomandibular joint (TMJ) and occlusion analysis. Methods of 3D modeling and analysis of the symmetry of the skull were used, as well as the Hamburg test to assess the functional state of the TMJ. The results of the clinical examination were subjected to a hierarchical cluster analysis. Results and discussion: Based on the results of the survey, a group of key risk factors affecting the development of occlusive disorders was identified. The analysis of the data showed that the most significant components are the muscular, articular and jaw complexes, including electromyographic parameters, the size of the articular gap and occlusal contacts. The average value of the Hamburg test among patients was 3.79±0.73 points, which indicates a high prevalence of TMJ dysfunction. Cluster analysis made it possible to identify three main groups of risk factors, including muscle, joint and dental signs. Conclusion: The identification of risk factors for the development of occlusive disorders plays a key role in improving the diagnosis and treatment of patients. The use of cluster analysis and modern technologies, such as 3D modeling and artificial intelligence, makes it possible to increase the effectiveness of prevention and treatment of this pathology.