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This study aims to evaluate whether an artificial intelligence application called Looksmaxxing AI will be able to correctly identify chin deviation (chin asymmetry) from standard frontal facial photographs. A total of 540 photographs will be included in the study. The eye areas will be covered to protect identity. Each photo will be analyzed by the AI, and its answers will be compared with clinical reality. The accuracy of two versions of the software (Looksmaxxing 4o and 5) will be assessed. The results may help show whether simple photo-based analysis can support early detection of chin asymmetry, especially in areas with limited access to orthodontic examination.
This observational study will investigate the accuracy of the Looksmaxxing AI application in detecting chin deviation based on standardized frontal facial photographs. A total of 540 images will be included. To protect identity, the eye areas of all participants will be covered. The AI will be asked to determine whether chin deviation is present relative to the midline of the face (excluding the nose).
The study population will consist of two equally sized groups:
270 individuals with clinically observed chin deviation (laterognathia)
270 individuals with clinically normal chin position
The responses provided by Looksmaxxing AI will be compared with the clinical reality and recorded for accuracy. Both versions of the software, Looksmaxxing 4o and Looksmaxxing 5, will be analyzed to evaluate their level of consistency with clinical findings. Results will be reported as accuracy percentages. In addition, prediction accuracy will be compared between individuals with and without chin deviation, and the statistical significance of the difference will be tested using the Chi-square method.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| With Chin Deviation | Participants clinically diagnosed with chin deviation (laterognathia) based on frontal facial examination. |
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| Without Chin Deviation | Participants with clinically normal chin position confirmed by frontal facial examination. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Looksmaxxing AI Facial Analysis | Other | Participants' standardized frontal facial photographs will be analyzed using the Looksmaxxing AI application (versions 4o and 5) to determine the presence or absence of chin deviation relative to the facial midline. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of Looksmaxxing AI in Detecting Chin Deviation | The proportion of correct classifications (presence or absence of chin deviation) made by the Looksmaxxing AI application compared to the clinical gold standard, expressed as a percentage. | At study completion (October 2025) |
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Inclusion Criteria:
No major wound or scar in the facial or neck region
No history of previous orthodontic treatment
For male participants: absence of a long beard that could affect the appearance of the chin
Availability of standardized frontal facial photographs taken in natural head position
Exclusion Criteria:
Blurred or low-quality photographs with reduced clarity
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Standardized frontal facial photographs of 90 individuals (540 images in total) will be analyzed. The study population consists of two equal groups: 270 photographs from individuals with clinically observed chin deviation (laterognathia) and 270 photographs from individuals with clinically normal chin position.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Necmettin Erbakan University | Konya | Konya | 42090 | Turkey (Türkiye) |
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| Label | URL |
|---|---|
| This article reports the use of deep learning for automatic classification of orthodontic photographs, providing methodological background relevant to AI-based facial asymmetry detection. | View source |
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Individual participant data (facial photographs) will not be shared due to privacy concerns and the risk of re-identification. Only aggregated and anonymized results will be published.
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