Deep Learning for Gummy Smile Segmentation

NCT06965387 | Recruiting

• 1 other identifier: E-77082166-604.01-881629
• Study Type: observational
• Target: 1,740
• Locations: 1 country
• Sites: 1

Brief Summary

A gummy smile (excessive visibility of the gums when smiling) is not merely an aesthetic issue but also an important parameter in terms of periodontal health. Current evaluation methods are subjective and non-standardized, leading to limitations in both clinical accuracy and patient communication. In recent years, AI-based models have begun to be effectively used in dental image analysis and diagnostic processes. This study aims to develop an AI-supported objective and reproducible analysis model capable of evaluating gummy smile from both aesthetic and periodontal perspectives using a unique dataset composed of images obtained through standard clinical protocols and labeled by the same expert. Individuals aged 12 years or older with no maxillary anterior (teeth #13-23) tooth loss will be included in the study. Patients with missing anterior maxillary teeth (teeth #13-23), significant anatomical pathologies, or smile-interfering factors (e.g., facial piercings, orthodontic appliances, facial hair) will be excluded. Standardized frontal photographs will be taken using a single device (iPhone 15) to ensure consistency in resolution, lighting, and color balance. Images will be captured from a fixed distance of 15 cm with participants in an upright position, eyes facing forward, and heads aligned to the Frankfurt Horizontal Plane. To maintain standardization, the smartphone's grid lines will be used to align the horizontal line with the pupils and vertical lines with the nasal alae. Images of high, average, and low smile lines will be labeled by a periodontist using the web-based annotation tool MakeSense. Visible gingival areas will be annotated as polygons bounded superiorly by the lower border of the upper lip and inferiorly by the gingival margin. For participants with high smile lines, gingival display will be measured using ImageJ (National Institutes of Health, Bethesda, MD, USA), with calibration performed via a periodontal probe embedded in each photo. A pixel-to-millimeter conversion factor will be derived and applied to measurements between the upper lip and gingival margin in the anterior maxillary sextant (teeth #13-23). Distances between paired landmarks (points 7-13, 8-14, 9-15, 10-16, 11-17, 12-18) will be measured in millimeters. AI-based segmentation outputs (via MakeSense) will be statistically compared to ImageJ measurements to assess correlation.

Conditions

Gummy Smile

Outcome Measures

Primary Outcomes (1)

• Evaluation of gummy samples using artificial intelligence

  From enrollment to the end of treatment at 4 months

Study Arms (1)

• High smile

Gingival margins and cervical areas are clearly visible above the upper lip line.

Eligibility Criteria

• Age: 12 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who presented to the Department of Periodontology, Faculty of Dentistry, Gazi University, for routine dental evaluation and treatment

You may qualify if:

• Individuals aged 12 years or older with no maxillary anterior (teeth #13-23) tooth loss will be included in the study.

You may not qualify if:

• Patients with missing anterior maxillary teeth (teeth #13-23), significant anatomical pathologies, or smile-interfering factors (e.g., facial piercings, orthodontic appliances, facial hair) will be excluded.

Sponsors & Collaborators

• Gazi University: lead
• The Scientific and Technological Research Council of Turkey: collaborator

Study Sites (1)

Gazi University

Ankara, Cankaya, 06490, Turkey (Türkiye)

RECRUITING

Study Officials

• Zeynep Turgut Cankaya, Associate Professor

  Gazi University

  STUDY DIRECTOR

Central Study Contacts

Zeynep Turgut Cankaya, Associate Professor

CONTACT

+90 5333899618; zeynepturgut@gazi.edu.tr

Gulenay Colak, Research Assistant

CONTACT

+905072637113; gulenaycolak@gazi.edu.tr

Study Design

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

Study Record Dates

• First Submitted: May 2, 2025
• First Posted: May 11, 2025
• Study Start: January 12, 2025
• Primary Completion: May 15, 2025
• Study Completion: May 20, 2025
• Last Updated: May 21, 2025

Record last verified: 2025-05

Locations

TU (1)