OpenbiteAIASOBPNCT06992908
Reliability of Artificial Intelligence for Treatment Decision for Adult Skeletal Open Bite Patients
An observational study of Openbite and Artifical Intelligence, run by Cairo University at 1 site in Egypt.
Trial summary
This study evaluated a specially designed AI model developed by a programmer using x-ray readings and corresponding treatment decisions from 70% of the cases (either orthodontic treatment only or orthodontic treatment with surgery). For the evaluation, we will use the remaining 30% of cases. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Openbite, Artifical Intelligence
- How many people
- 53 participants
- Where
- 1 siteEgypt
- Study dates
- 12 May 2024to 15 Mar 2025
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult anterior skeletal open bite patients with at least 3mm opening.
Who may be able to join
- Moderate to severe Adult patients with anterior skeletal open bite (at least 3mm opening)
- Completed their treatment successfully.
- Well-documented cases with comprehensive preoperative and postoperative lateral cephalometric x-rays were considered.
Who cannot join
- Patient below 18 years.
- Improperly finished orthodontic treatment.
- Incomplete documentation.
- cleft lip and palate patient, patient with syndromes.
- Dental open bite.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Reliability of Artificial Intelligence for Treatment Decision Recommendation for Adult Skeletal Open Bite Patients: A Diagnostic Accuracy Pilot Study
This study evaluated a specially designed AI model developed by a programmer using x-ray readings and corresponding treatment decisions from 70% of the cases (either orthodontic treatment only or orthodontic treatment with surgery).
For the evaluation, we will use the remaining 30% of cases. "Subsequently, to assess its performance, the model was tested on the remaining 30% of cases. The programmer provided only the X-ray readings as input. The AI model was then tasked with classifying.
Read the detailed description
In this study, all patients were treated completely with a well-finished result by expert orthodontists. This study evaluates whether an artificial intelligence (AI) model can enhance treatment decisions for adult skeletal open bite cases by predicting the optimal intervention, either orthognathic surgery or camouflage, using cephalometric readings as input data.
First, a total of 53 cases were analyzed, which were divided into two groups:
70% were allocated to the machine learning group (MLG), while the remaining 30% constituted the test group (TG). Cephalometric analysis for all patients was performed using Dolphin Imaging 11.5 Premium software, along with determining the appropriate treatment decision, either camouflage or orthognathic surgery.
The data obtained from MLG serves as training data for the AI model to classify cases based on their cephalometric data, whether for camouflage or orthognathic surgery. The input data consisted of cephalometric readings along with a decision.
Second, after machine learning, validation takes place to examine the ability of the machine to make decisions through some cases from MLG.
The third step will evaluate the machine's ability to accurately determine case decisions based on cephalometric readings. The results produced by the machine will be compared to the actual decisions made, as all these cases were treated under the supervision of orthodontic professors.
- Conditions
- Openbite, Artifical Intelligence
- Medical subject headings
- Open Bite
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 |
|---|---|---|
| Group 1: MLG acts as learning group. | Cephalometric readings parameters of cases with their decisions will be used in this group for training the machine to be able to make a decision. | |
| Group 2: TG acts as a test group | The programmer will put parameters as input and then ask the AI model to decide on an output of the model, either camouflage or surgery. |
What is being measured
The primary outcome is the main question the study is designed to answer.
accuracy of diagnostic test
In this study, the investigators have only one outcome, which is the accuracy of a diagnostic test.
This test is based on the comparison of the results of machine learning decisions with a conventional method, which is an expert orthodontist's decision.
Accuracy = (true positives + true negatives) / (total) A true positive will be the correct prediction of camouflage. A true negative will be the correct prediction of orthognathic surgery. Total means the total test set cases. All cases are randomly distributed. The investigators use the skeletal and dental cephalometrical parameters , some of which are angles measured by degrees, and others are linear measurements measured by millimeters.
SNA SNB ANB SN/PP SN/MP Maxillary-mandibular plane angle Anterior facial height Posterior facial height Jarabak ratio Gonial angle Lower anterior facial height Open bite Maxillary anterior teeth inclination Mandibular anterior teeth inclination
Measured: through study completion, an average of 1 year".
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 May 2025
- Study start12 May 2024
- Primary completion15 Jan 2025
- Study completion15 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OpenbiteStudies of the same condition, open and under way.
- More trials run by Cairo 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 53 participants (actual)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Marwa Saeed Abdullah Saeed Badyah, Cairo University
- Investigators
- Dina Osman, Associate Professor, Study director, supervisorMostafa El-Dawlatly, Associate Professor, Study director, supervisor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06992908
- Sponsor's protocol ID
- Adult skeletal open bite cases
- Other IDs
- Self-funded by me.
- Acronym
- AIASOBP
- Status last verified
- May 2025
- First submitted
- 24 Apr 2025
- Last updated
- 28 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Reliability of Artificial Intelligence for Treatment Decision Recommendation for Adult Skeletal Open Bite Patients: A Diagnostic Accuracy Pilot Study
- ClinicalTrials.gov ID
- NCT06992908
- Acronym / sponsor ID
- AIASOBP / Adult skeletal open bite cases
- Conditions
- Openbite, Artifical Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 53 participants (actual)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 12 May 2024 (actual)
- Primary completion
- 15 Jan 2025 (actual)
- Study completion
- 15 Mar 2025 (actual)
- First posted
- 28 May 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 evaluated a specially designed AI model developed by a programmer using x-ray readings and corresponding treatment decisions from 70% of the cases (either orthodontic treatment only or orthodontic treatment with surgery).
For the evaluation, we will use the remaining 30% of cases. "Subsequently, to assess its performance, the model was tested on the remaining 30% of cases. The programmer provided only the X-ray readings as input. The AI model was then tasked with classifying.
Detailed description
In this study, all patients were treated completely with a well-finished result by expert orthodontists. This study evaluates whether an artificial intelligence (AI) model can enhance treatment decisions for adult skeletal open bite cases by predicting the optimal intervention, either orthognathic surgery or camouflage, using cephalometric readings as input data.
First, a total of 53 cases were analyzed, which were divided into two groups:
70% were allocated to the machine learning group (MLG), while the remaining 30% constituted the test group (TG). Cephalometric analysis for all patients was performed using Dolphin Imaging 11.5 Premium software, along with determining the appropriate treatment decision, either camouflage or orthognathic surgery.
The data obtained from MLG serves as training data for the AI model to classify cases based on their cephalometric data, whether for camouflage or orthognathic surgery. The input data consisted of cephalometric readings along with a decision.
Second, after machine learning, validation takes place to examine the ability of the machine to make decisions through some cases from MLG.
The third step will evaluate the machine's ability to accurately determine case decisions based on cephalometric readings. The results produced by the machine will be compared to the actual decisions made, as all these cases were treated under the supervision of orthodontic professors.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group 1: MLG acts as learning group. | – | –Cephalometric readings parameters of cases with their decisions will be used in this group for training the machine to be able to make a decision. |
| Group 2: TG acts as a test group | – | –The programmer will put parameters as input and then ask the AI model to decide on an output of the model, either camouflage or surgery. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult anterior skeletal open bite patients with at least 3mm opening.
Key inclusion criteria (3 listed)
- Moderate to severe Adult patients with anterior skeletal open bite (at least 3mm opening)
- Completed their treatment successfully.
- Well-documented cases with comprehensive preoperative and postoperative lateral cephalometric x-rays were considered.
Key exclusion criteria (5 listed)
- Patient below 18 years.
- Improperly finished orthodontic treatment.
- Incomplete documentation.
- cleft lip and palate patient, patient with syndromes.
- Dental open bite.
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 of diagnostic test
Time frame: through study completion, an average of 1 year".
In this study, the investigators have only one outcome, which is the accuracy of a diagnostic test. This test is based on the comparison of the results of machine learning decisions with a conventional method, which is an expert orthodontist's decision. Accuracy = (true positives + true negatives) / (total) A true positive will be the correct prediction of camouflage. A true negative will be the correct prediction of orthognathic surgery. Total means the total test set cases. All cases are randomly distributed. The investigators use the skeletal and dental cephalometrical parameters , some of which are angles measured by degrees, and others are linear measurements measured by millimeters. SNA SNB ANB SN/PP SN/MP Maxillary-mandibular plane angle Anterior facial height Posterior facial height Jarabak ratio Gonial angle Lower anterior facial height Open bite Maxillary anterior teeth inclination Mandibular anterior teeth inclination
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 28 May 2025.
- Individual participant dataSharing planned: No
Related trials for Openbite
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IOA Preventing Occlusal Changes With MAD Use
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Investigate the Effectiveness of 3D-printed Shape Memory Aligners in the Treatment of Anterior Open Bite
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Stability of Orthodontic Treatment of Anterior Open-bite
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Single Palatal Temporary Anchorage Device for Anterior Open Bite
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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.
Dina Osman, Associate Professor study director
supervisor
Mostafa El-Dawlatly, Associate Professor study director
supervisor
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 Openbite and Artifical Intelligence, run by Cairo University at 1 site in Egypt.
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 3 inclusion and 5 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 28 May 2025. 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 Egypt. 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 of diagnostic test (time frame: through study completion, an average of 1 year".). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 28 May 2025.