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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.
Subjects will be selected according to the inclusion criteria previously listed, and they will present the needed records for participating in the study. After taking initial photos of the patients before treatment, these photos will be uploaded to the AI model to generate the prediction photo, the selected subjects for the prediction group will view their AI-predicted photo, and their motivation will be evaluated by a questionnaire.
The control group will not view a prediction photo; they will answer the motivation questionnaire only.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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
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| Name | Affiliation | Role |
|---|---|---|
| Shaza Hammad, professor | Mansoura University | Study Director |
| Marwa Shamaa, Professor | Mansoura University | Study Chair |
| Ahmed Kamel, Lecturer | Mansoura University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Mansoura university | Al Mansurah | Dakahlia Governorate | 35111 | Egypt |
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| Label | URL |
|---|---|
| Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review | View source |
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| 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. |
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| ID | Term |
|---|---|
| D019066 | Facies |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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