Clinical trial NCT06965387: Deep Learning for Gummy Smile Segmentation
Official title: Deep Learning-Based Evaluation of Gummy Smile: Development and Validation of a Segmentation Model
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Gummy Smile
Lead sponsor: Gazi University
Sponsor class: OTHER
Collaborators: name: The Scientific and Technological Research Council of Turkey
First posted: 2025-05-11
Enrollment: 1740
Sex: ALL
Minimum age (years): 12
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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

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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Evaluation of gummy samples using artificial intelligence
timeFrame: From enrollment to the end of treatment at 4 months
Secondary outcomes:
Other outcomes:
Locations:
city: Ankara
country: Turkey
facility: Gazi University
state: Cankaya
Study officials:
affiliation: Gazi University
name: Zeynep Turgut Cankaya, Associate Professor
role: STUDY_DIRECTOR
References: