Identification of Risk Factors for the Development of Disorders of Occlusal Relationships

Abstract

Violations of occlusal relationships are an urgent problem in dental practice due to their high prevalence, complexity of diagnosis and treatment, as well as their association 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 occlusal relationships. Materials and methods. A randomized controlled trial was conducted with 72 patients aged 18 to 44 years. The study did not include patients with acute infectious diseases, decompensated chronic diseases, cancer, mental disorders, and periodontal inflammation. The assessment included a clinical examination, cone beam computed tomography, electromyography, ultrasound examination of the temporomandibular joint, and occlusion analysis. Methods of 3D modeling and analysis of cranial symmetry were used, as well as the Hamburg test to assess the functional state of the temporomandibular joint. The results of the clinical examination were subjected to a hierarchical cluster analysis. Results and discussion. Based on the results of the examination, a group of key risk factors influencing 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 3.82 ± 0.71 points, indicating a high prevalence of the temporomandibular joint dysfunction. Cluster analysis allowed us to identify three main groups of risk factors, including muscle, joint, and dental signs. Conclusion. Identification of risk factors for occlusive disorders plays a key role in improving 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.