Assesment of Ethnic Bias in an Artificial Intelligence Based Orthodontic Diagnosis System

This study investigates how artificial intelligence (AI) can be used to diagnose skeletal malocclusions. AI is a computer technology that can learn from data, recognize patterns, and then make predictions. It is already being used in different areas of medicine and dentistry, but its accuracy and fairness may depend on the data it was trained with. In orthodontics, skeletal problems are usually classified into four main groups: Class I (normal skeletal relationship), Class II Division 1, Class II Division 2 (upper jaw forward or lower jaw retruded or both ), and Class III (lower jaw forward in relation to the upper jaw or upper jaw retruded relation to lower jaw or both). For this study, an AI system was trained with profile photographs and X-rays of 3,280 Turkish patients. The system learned to recognize all four types of skeletal relationships: Class I, Class II Division 1, Class II Division 2, and Class III. After the training, the AI was tested on new groups of patients whose images were not part of the training set. This included a group of Turkish patients and another group of patients from different ethnic backgrounds treated in Belgium. By comparing the AI's results in these two groups, researchers will be able to see whether the system works equally well for people of different ethnicities. The patient data used in this study is completely anonymized. Only pre-treatment profile photographs and lateral cephalometric X-rays are included. The images must comply with quality standards such as being taken in natural head position, with a clear background and without beard, scars, or previous orthodontic treatment that might interfere with recognition. Patients whose photographs do not meet these standards are excluded. This research is conducted as a cross-sectional diagnostic accuracy study (single-gate, multi-center). The diagnostic performance of the AI system is evaluated using accuracy, recall (sensitivity), precision, false positive rate, F1 score, and area under the curve (AUC). The aim of this study is to evaluate whether an AI-based system used by patients for orthodontic diagnosis provides fair and reliable results across different ethnic groups.