Comparative analysis of morphometric parameters of the stomatognatic system in patients with skeletal Class III malocclusion based on CBCT

Abstract

The implementation of deep learning computer technologies in dentistry aims to improve the precision, comfort, and predictability of treatment. A detailed, ready-to-use result can be obtained in 4—5 minutes. The accuracy of artificial intelligence in dentistry ranges from 85% to 99%, depending on the clinical task. Our BonaByte software (BonaByte LLC, Russia) significantly reduces the time spent on the diagnostic stage—cephalometry.
Materials and methods.
The study included 139 individuals of both sexes aged 18 to 44 years (mean age 25 years), of whom 80 subjects (32 males and 48 females) with Class I malocclusion and 59 patients (25 males and 34 females) with Class III malocclusion. A 3D cephalometric analysis of linear and angular parameters of the craniofacial skeletal structures was performed in the BonaByte software using an author’s method in an automated mode powered by artificial intelligence.
Results.
Statistically significant differences in dental-maxillary parameters were identified in both male and female subjects with skeletal Class III malocclusion. In males, the skeletal Class III malocclusion was associated with mandibular macrognathia, whereas in females it was associated with mandibular pro- and macrognathia. Both study groups demonstrated a predominance of the vertical skeletal growth pattern.
Conclusion.
In the diagnosis and treatment of patients with skeletal Class III malocclusion, 3D cephalometric analysis is essential due to its ability to provide objective data sufficient for establishing a definitive diagnosis and developing a treatment plan, taking into account the existing parameter differences between males and females, and the use of programs based on deep machine learning simplifies all the above steps.