Clinical Research Directory

Clinical Evaluation of Gingivalstat Approach Compared to Conventional Esthetic Crown Lengthening on the Stability of The Gingival Margin in Patients With Altered Passive Eruption.

When performing crown lengthening surgery, especially in the esthetic zone, the positional stability of gingival tissues is considered a prime important goal. The rebound of gingival margins after surgery can result in compromising the esthetic outcome and patient satisfaction. It was proven throughout the literature that there are many factors that may influence the stability of gingival margin position after surgery such as: the surgical technique being performed, experience of the clinician, periodontal phenotype and distance of flap with respect to the alveolar crest.

Deep Learning for Gummy Smile Segmentation

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.

Gummy Smile and Artificial Intelligence

A smile, as a nonverbal communication tool, is based on a balanced relationship between the teeth and the surrounding hard and soft tissues. The literature highlights the need for the evaluation of smile design using artificial intelligence (AI), suggesting that AI-assisted assessments could play a crucial role in all relevant stages of clinical parameters associated with gingival smile analysis. A gummy smile (GS) is defined as the excessive display of gingival tissue exceeding 3 mm during smiling. The hypothesis of this study is based on the assumption that clinical data obtained for the analysis and diagnosis of gingival visibility can be accurately and reliably evaluated using AI-supported algorithms. To date, no study has been found in the literature that diagnoses GS using AI, predicts its etiological factors, and assesses its implications for treatment planning.

Assessment of The Accuracy of Complete Crown 3D Superimposition Technique Relative to The Gold Standard Technique for Digital Quantification of Volumetric Soft Tissue Changes in The Esthetic Zone

The aim of this study is to assess the precision of the complete crown technique employed for the superimposition of 3D models, specifically in the quantitative volumetric evaluation of gingival tissues. This technique was initially introduced in an in-vitro study by Dritsas et al. (2023) as a means to achieve an accurate, reproducible, and simple method for quantification of gingival recession. This study will involve the analysis of digital models in. ply format acquired through intraoral scanning of patients scheduled for esthetic crown lengthening, with predefined inclusion and exclusion