歯周組織型分類の新規開発：硬組織および軟組織パラメータを統合したデータ駆動型クラスター分析

To combine quantitative measures of gingival thickness (GT), keratinized tissue width (KTW), and alveolar buccal bone thickness (ABT) for developing a novel classification of periodontal phenotype through a clustering analysis, using two digital images.

This cross-sectional study comprised 180 subjects with a total of 1080 maxillary anterior teeth. GT and ABT were assessed utilizing superimposed Cone Beam Computed Tomography (CBCT) and intraoral scan data, while KTW was obtained from intraoral images employing a correction factor. Hierarchical cluster analysis (HCA) was conducted to discern phenotypic categories. Cutoff values for GT, KTW, and ABT were derived from cluster boundaries to provide a clinically applicable scoring system.

Cluster analysis identified four statistically significant phenotypic groupings (p < 0.001). Only 48.9% of instances accounted for conventional "thin" or "thick" phenotypes, while 51.1% exhibited mixed characteristics. Cutoff values were established as GT = 0.85 mm, KTW = 3.0 mm, and ABT = 1.0 mm. Based on these cutoffs, a scoring system (0-3) was developed to classify individuals as fully thin, majority-thin (gingival thin/ bone thick)" and "majority-thick (gingival thick/ bone thin), or fully thick. This system demonstrated good internal consistency, with moderate to high coefficients of determination (R²) across parameters.

This study presents a comprehensive, data-driven classification of periodontal phenotype using quantitative evaluations of GT, KTW, and ABT, providing an innovative viewpoint on phenotype-based diagnostics. The proposed system offers a clinically relevant framework for phenotype-based diagnostics, risk evaluation, and individualized treatment planning.

By combining soft (GT, KTW) and hard (ABT) tissue parameters with 3D imaging and clustering analysis, this classification provides clinicians with a practical and quantitative tool for assessing periodontal phenotype, thereby supporting accurate diagnosis and evidence-based decision-making across multiple dental disciplines.