Class III Malocclusion

This RCT aims to evaluate the effectiveness of a novel 3D printed reverse twin block appliance in the management of maxillary deficiency in growing patients having class III malocclusion. The clinical outcomes of this appliance will be compared with the conventional reverse twin block appliance over 6 months treatment period. primary objective is evaluating of the effectiveness of 3DRTB in correcting sagittal discrepancies due to maxillary deficiency in class III malocclusion compared to conventional one while secondary objectives are assessment of transverse and vertical jaw relationship produced by 3DRTB compared to CRTB and to evaluate the soft tissue profile of class III patients and to evaluate the maxillary and mandibular incisor inclination, also assessment of patient perception with both appliances by using questionnaire and finally to assess the impact of 3DRTB on oral health compared to CRTB by using oral hygiene index.

The project involves a 10-year prospective longitudinal cohort multicentre study where orthodontic treatment and treatment effect is followed in children with Cl III due to maxillary retrognathia who undergo standardized orthodontic interceptive treatment. Standard clinical procedures are followed as usual at each clinic where standard diagnostic and follow up materials are taken before treatment, after treatment and follow ups after 5 and 10 years. The project will not introduce or test new treatment modalities, additional procedures or behavioural regulation in relation to normal clinical practice or new medical devices. The standard diagnostic and follow up materials in each clinic include examination of the dentition, occlusion, craniofacial morphology, and orofacial function. The analyses are based on the following standardized registration methods which are carried out on all the patients. Information from the patient record is included. The patient related outcome measurements will be assessed by five different questionnaires. No biological material is collected for the project.

This study explores how artificial intelligence (AI) can be used in orthodontics, which is the area of dentistry that focuses on correcting jaw and bite problems. AI is a computer technology that can learn from large amounts of data and then make predictions or decisions. It is already being tested in medicine and dentistry to help doctors and dentists diagnose conditions. For this study, the AI system was trained using photographs and X-rays from patients in Turkey. The system learned to recognize specific orthodontic skeletal malocclusions. After the training stage, the AI was tested in two groups: one group included Turkish patients whose records were not used in training, and the other group included patients from different ethnic backgrounds who were treated at a clinic in Belgium. This design allows researchers to see if the AI works equally well for people of different backgrounds. Only photographs and X-rays taken before orthodontic treatment are used in the study, and all data are anonymized so that no personal information is shared. The images must meet certain quality standards. For example the head must be in natural position, with no beards, scars, or previous orthodontic treatment that might affect the image. Patients who do not meet these criteria are not included. The AI program analyzes the profile photographs, prepares them for evaluation by adjusting and standardizing the images, and then tries to decide each patient has which malocclusion. The results from Turkish patients and patients from other ethnic groups are compared to see if the system makes fair and accurate decisions for everyone. The purpose of this study is not to test a new treatment, but to understand how well AI can recognize orthodontic problems in different populations. This information is important because AI systems are increasingly being used in healthcare, and they need to be fair and accurate for all patients, not just those from one group. By participating, patients help researchers learn whether AI in orthodontics is reliable across diverse communities. This knowledge can guide future improvements in AI technology, ensuring that it supports orthodontists in providing safe, equal, and effective care for everyone.