Effect of an AI Prediction Model of Facial Changes After Orthodontic Treatment of Patients With Bimaxillary Protrusion

NCT07066423 | Active Not Recruiting

• 1 other identifier: PHD Candidate Kholoud Mandour
• Study Type: interventional
• Target: 45
• Locations: 1 country
• Sites: 1

Brief Summary

After developing an Artificial Intelligence model to predict the facial changes of patients with bimaxillary protrusion following orthodontic extraction of premolars and tooth retraction, participants will view a prediction in the form of a picture generated by the AI model, and their motivation will be measured using a questionnaire. Their satisfaction will be evaluated using another questionnaire after they complete their treatment. A comparison will then be made between the actual results and the AI-predicted results.

Conditions

Bimaxillary Protrusion

Keywords

Bimaxillary protrusion; prediction; Artificial intelligence; retraction; facial

Outcome Measures

Primary Outcomes (1)

• Participants motivation for treatment

  The motivation questionnaire will be performed to test if the prediction picture will affect their motivation to continue the treatment and proceed with the extraction decision. The questions will be in the multiple-choice format. Open-ended comments were allowed at the end of each question to collect data concerning reasons, barriers, and encouraging factors that the authors may not have considered. The questionnaire Arabic version will be used as it was validated by authors Felemban et al. in 2022. Based on the English version used by Chambers et al. 2019 and Laothong et al. in 2017, a modification will be added to the questionnaire regarding a question about how the prediction picture affected their motivation. After the treatment of each case, a 5-point Likert scale will be used to measure their satisfaction with the treatment and whether the net result matches the AI prediction, with 1 for not satisfied at all and 5 for satisfied.

  from three weeks to six months

Study Arms (2)

Ai prediction group

EXPERIMENTAL

The participant group that will be exposed to prediction photos and measure their motivation for the four 1st premolars extraction decision, also undergoes the orthodontic treatment.

Other: Ai prediction photo of the patients; Procedure: Fixed orthodontic brackets with premolars extraction

Control group

ACTIVE COMPARATOR

Subjects in this group will receive orthodontic treatment and answer a questionnaire about their motivation and satisfaction with the treatment, but they will not be exposed to AI prediction photos.

Procedure: Fixed orthodontic brackets with premolars extraction

Interventions

Ai prediction photo of the patients OTHER

Ai prediction photo generated by specially designed ai model to help patients see how they will look after orthodontic extraction and retraction

Ai prediction group

Fixed orthodontic brackets with premolars extraction PROCEDURE

Subjects will undergo orthodontic fixed brackets treatment, four premolar extractions from the mandibular and maxillary arches, followed by anterior segment retraction. This treatment will achieve dental alignment, profile, and facial enhancement.

Ai prediction group; Control group

Eligibility Criteria

• Age: 16 Years - 30 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• Adult cases age range from 16 to 30 years old
• Skeletal Bimaxillary protrusion with SNA and SNB increased
• Dental bimaxillary protrusion with U1- SN and IMP a increased angles
• Class I canine and molar angle classification
• Free from any systemic disease
• free from any dental surgery

You may not qualify if:

• Clefts of the lip or palate and craniofacial syndromes; orthognathic patients will not be included because they do not represent the general adult orthodontic population

Sponsors & Collaborators

• Mansoura University: lead

Study Sites (1)

Mansoura university

Al Mansurah, Dakahlia Governorate, 35111, Egypt

Location

Related Links

• Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review

MeSH Terms

Conditions

Facies

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Shaza Hammad, professor

  Mansoura University

  STUDY DIRECTOR

• Marwa Shamaa, Professor

  Mansoura University

  STUDY CHAIR

• Ahmed Kamel, Lecturer

  Mansoura University

  STUDY CHAIR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PHD candidate

Study Record Dates

• First Submitted: June 2, 2025
• First Posted: July 15, 2025
• Study Start: November 1, 2023
• Primary Completion: September 15, 2025
• Study Completion: November 1, 2025
• Last Updated: July 15, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

EG (1)