Class I MalocclusionNCT07162753
Assesment of Ethnic Bias in an Artificial Intelligence Based Orthodontic Diagnosis System
An observational study of Class I Malocclusion and Class II Div 1 Malocclusion, run by Bezmialem Vakif University at 1 site in Turkey (Türkiye).
Trial summary
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. Read more about this study
- Who is it for?
- Ages 4 years and olderHealthy volunteers can take part
- Conditions the study is for
- Class I Malocclusion, Class II Div 1 Malocclusion, Class II Division 2 Malocclusion
- How many people
- 10,000 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 6 Jan 2025to 12 Sep 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 4 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: 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 population.
Who may be able to join
None listed.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Bezmialem Vakıf University
Istanbul, Fatih, 34020
About this study
Official title: Detection of Racial Bias in an Artificial Intelligence Based Orthodontic Diagnosis System
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.
Read more
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 populations 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.
Read the detailed description
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 population and another group of patients from different population 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 populations.
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 populations.
- Conditions
- Class I Malocclusion, Class II Div 1 Malocclusion, Class II Division 2 Malocclusion, Class III Malocclusion
- Keywords
- Artificial Intelligence, Deep Learning, Machine Learning, Orthodontics, AI generalizability
- Medical subject headings
- Malocclusion, Angle Class I, Malocclusion, Angle Class II, Malocclusion, Angle Class III
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Turkish Citizen's Control Group (Hold-out) | The AI system was trained with data from 7,000 people. To test whether the AI has bias, the system should be evaluated both on a group of Turkish population who were not included in the training data and on patients from different populations. | |
| Individuals from different populations | Test set, consisting of individuals from different populations. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Accuracy (proportion of correctly classified cases) will be compared between Turkish test set, and non-Turkish cohort from different ethnic backgrounds.
Measured: At baseline (retrospective single time-point data analysis)
Secondary outcomes (4)
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
Measured: At baseline (retrospective single time-point data analysis)
Recall
Percentage of true Class III cases correctly identified by AI
Measured: At baseline (retrospective single time-point data analysis)
False Positive Rate
Rate of negative cases incorrectly classified as positive by AI, compared across cohorts.
Measured: At baseline (retrospective single time-point data analysis)
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.
Measured: At baseline (retrospective single time-point data analysis)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2025
- Study start6 Jan 2025
- Primary completion12 Sep 2026
- Study completion12 Sep 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Class I MalocclusionStudies of the same condition, open and under way.
- More trials run by Bezmialem Vakif UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 10,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bezmialem Vakif University
- Sponsor type
- University, hospital or other
- Collaborators
- PEBS DENTAL CLINIC
- Responsible party
- Principal investigator, Aslı Eker Davut, Bezmialem Vakif University
- Investigators
- Banu Kılıç, Doctor of Orthodontics, Study director, Bezmialem Vakif University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07162753
- Sponsor's protocol ID
- E-54022451-050.04-177270
- Status last verified
- Sep 2026
- First submitted
- 26 Aug 2025
- Last updated
- 15 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The data will be shared on reasonable request to the corresponding author.
Publications
Allareddy V, Oubaidin M, Rampa S, Venugopalan SR, Elnagar MH, Yadav S, Lee MK. Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthod Craniofac Res. 2023 Dec;26 Suppl 1:124-130. doi: 10.1111/ocr.12721. Epub 2023 Oct 17.
PubMed 37846615Kilic B, Ibrahim AH, Aksoy S, Sakman MC, Demircan GS, Onal-Suzek T. A family-centered orthodontic screening approach using a machine learning-based mobile application. J Dent Sci. 2024 Jan;19(1):186-195. doi: 10.1016/j.jds.2023.05.001. Epub 2023 May 17.
PubMed 38303845
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Racial Bias in an Artificial Intelligence Based Orthodontic Diagnosis System
- ClinicalTrials.gov ID
- NCT07162753
- Acronym / sponsor ID
- E-54022451-050.04-177270
- Conditions
- Class I Malocclusion, Class II Div 1 Malocclusion, Class II Division 2 Malocclusion, Class III Malocclusion
- Keywords
- Artificial Intelligence, Deep Learning, Machine Learning, Orthodontics, AI generalizability
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 10,000 participants (actual)
- Lead sponsor
- Bezmialem Vakif UniversityUniversity, hospital or other
- Collaborators
- PEBS DENTAL CLINIC
- Study start
- 6 Jan 2025 (actual)
- Primary completion
- 12 Sep 2026 (actual)
- Study completion
- 12 Sep 2026 (actual)
- First posted
- 9 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 populations 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.
Detailed description
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 population and another group of patients from different population 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 populations.
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 populations.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Turkish Citizen's Control Group (Hold-out) | – | –The AI system was trained with data from 7,000 people. To test whether the AI has bias, the system should be evaluated both on a group of Turkish population who were not included in the training data and on patients from different populations. |
| Individuals from different populations | – | –Test set, consisting of individuals from different populations. |
Who may qualify
- Ages
- 4 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 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 population.
Key inclusion criteria (0 listed)
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy
Time frame: At baseline (retrospective single time-point data analysis)
Accuracy (proportion of correctly classified cases) will be compared between Turkish test set, and non-Turkish cohort from different ethnic backgrounds.
4 secondary outcomes
Precision (Positive Predictive Value)
Time frame: At baseline (retrospective single time-point data analysis)
Recall
Time frame: At baseline (retrospective single time-point data analysis)
False Positive Rate
Time frame: At baseline (retrospective single time-point data analysis)
F1 score
Time frame: At baseline (retrospective single time-point data analysis)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Sep 2026.
- Background: PubMed 37846615Allareddy V, Oubaidin M, Rampa S, Venugopalan SR, Elnagar MH, Yadav S, Lee MK. Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthod Craniofac Res. 2023 Dec;26 Suppl 1:124-130. doi: 10.1111/ocr.12721. Epub 2023 Oct 17.
- Background: PubMed 38303845Kilic B, Ibrahim AH, Aksoy S, Sakman MC, Demircan GS, Onal-Suzek T. A family-centered orthodontic screening approach using a machine learning-based mobile application. J Dent Sci. 2024 Jan;19(1):186-195. doi: 10.1016/j.jds.2023.05.001. Epub 2023 May 17.
- Individual participant dataSharing planned: Yes. The data will be shared on reasonable request to the corresponding author.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Banu Kılıç, Doctor of Orthodontics study director
Bezmialem Vakif University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Class I Malocclusion and Class II Div 1 Malocclusion, run by Bezmialem Vakif University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: At baseline (retrospective single time-point data analysis)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07162753, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Sep 2026.