Reliability of Artificial Intelligence for Treatment Decision for Adult Skeletal Open Bite Patients

NCT ID: NCT06992908

Last Updated: 2025-05-28

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 53 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-05-12
• Study Completion Date: 2025-03-15

Brief 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. "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.

Conditions

Openbite; Artifical Intelligence

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

No interventions assigned to this group

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.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• Patient below 18 years.
• Improperly finished orthodontic treatment.
• Incomplete documentation.
• cleft lip and palate patient, patient with syndromes.
• Dental open bite.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Cairo University

OTHER

Sponsor Role: lead

Responsible Party

Marwa Saeed Abdullah Saeed Badyah

Master's student.

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Dina Osman, Associate Professor

Role: STUDY_DIRECTOR

supervisor

Mostafa El-Dawlatly, Associate Professor

Role: STUDY_DIRECTOR

supervisor

Locations

Faculty of Dentistry, Cairo University

Cairo, , Egypt

Countries

Egypt

Other Identifiers

Self-funded by me.

Identifier Type: OTHER

Identifier Source: secondary_id

Adult skeletal open bite cases

Identifier Type: -

Identifier Source: org_study_id