Looksmaxxing AI for Chin Deviation Detection

NCT07197359 | Active Not Recruiting

• 1 other identifier: 2025/635
• Study Type: observational
• Target: 90
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Mandibular Asymmetry

Keywords

chin asymmetry; photographic analysis; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• 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)

Study Arms (2)

With Chin Deviation

Participants clinically diagnosed with chin deviation (laterognathia) based on frontal facial examination.

Other: Looksmaxxing AI Facial Analysis

Without Chin Deviation

Participants with clinically normal chin position confirmed by frontal facial examination.

Other: Looksmaxxing AI Facial Analysis

Interventions

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.

With Chin Deviation; Without Chin Deviation

Eligibility Criteria

• Age: 10 Years - 40 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• Absence of any craniofacial anomaly
• 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

You may not qualify if:

• Photographs in which the patient's face appears slightly angled or turned sideways
• Blurred or low-quality photographs with reduced clarity

Sponsors & Collaborators

• Konya Necmettin Erbakan Üniversitesi: lead

Study Sites (1)

Necmettin Erbakan University

Konya, Konya, 42090, Turkey (Türkiye)

Location

Related Links

• This article reports the use of deep learning for automatic classification of orthodontic photographs, providing methodological background relevant to AI-based facial asymmetry detection.

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research assistant

Study Record Dates

• First Submitted: September 21, 2025
• First Posted: September 29, 2025
• Study Start: September 1, 2025
• Primary Completion: September 30, 2025
• Study Completion: October 15, 2025
• Last Updated: October 2, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)