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NCT07162753
Assesment of Ethnic Bias in an Artificial Intelligence Based Orthodontic Diagnosis System
trial in Class I Malocclusion in 5,000 participants. Currently enrolling.
15 June 2026
Quick facts
| Lead sponsor | Bezmialem Vakif University |
|---|---|
| Status | Recruiting now |
| Study type | OBSERVATIONAL |
| Enrollment | 5,000 |
| Start date | 6 January 2025 |
| Primary completion | 15 June 2026 |
| Estimated completion | 11 August 2026 |
| Sites | 1 location across Turkey (Türkiye) |
Conditions studied
- Class I Malocclusion — all drugs for Class I Malocclusion →
- Class II Div 1 Malocclusion — all drugs for Class II Div 1 Malocclusion →
- Class II Division 2 Malocclusion — all drugs for Class II Division 2 Malocclusion →
- Class III Malocclusion — all drugs for Class III Malocclusion →
Sponsor
Bezmialem Vakif University
Who can join
4 and older, any sex, with Class I Malocclusion or Class II Div 1 Malocclusion. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT07162753
- Europe PMC full search
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07162753 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Bezmialem Vakif University
- Last refreshed: 5 December 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07162753.
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