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.
Patients with lateral profile photographs taken before starting orthodontic treatment
Profile photographs with a plain background
Patient in natural head position facing forward
No beard or mustache that could camouflage the profile
Blurry missing or non standard profile photographs
Presence of beard mustache scars cleft lip palate or soft tissue deformities that may significantly affect facial appearance
Previous orthodontic treatment history
One group consists of Turkish citizens who were not included in the AI training set. Another group consists of patients who visited the PEBS clinic in Brussels, Belgium, representing individuals from different ethnic backgrounds.
Other
Retrospective
Not listed
Accuracy
Accuracy (proportion of correctly classified cases) will be compared between Turkish test set, and non-Turkish cohort from different ethnic backgrounds.
Precision (Positive Predictive Value)
Percentage of cases labeled as Class III by AI that are truly Class III. Rate of AI-predicted positive cases that are truly positive, compared between Turkish group and non-Turkish ethnic group
Recall
Percentage of true Class III cases correctly identified by AI
False Positive Rate
Rate of negative cases incorrectly classified as positive by AI, compared across cohorts.
F1 score
Harmonic mean of precision and recall (range 0-1). Higher values indicate better overall performance, while lower values indicate poor balance between sensitivity and precision.
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Bezmialem Vakif University
Lead sponsor
PEBS DENTAL CLINIC
Collaborator
