An observational study in Openbite and Artifical Intelligence, sponsored by Cairo University. Completed at 1 site in Egypt. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-05-28.
Sponsored by Cairo University · Observational
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.
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.
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Adult anterior skeletal open bite patients with at least 3mm opening.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
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.
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.
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
Time frame: through study completion, an average of 1 year".
Plan to share: No
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This study is completed, as verified in May 2025. You cannot join it, but the record below documents what was studied.
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