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Objective <p>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.</p> Materials and methods <p>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.</p> Results <p>Cluster analysis identified four statistically significant phenotypic groupings (<i>p</i> &lt; 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&#xa0;mm, KTW = 3.0&#xa0;mm, and ABT = 1.0&#xa0;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.</p> Conclusion <p>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.</p> Clinical relevance <p>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.</p>

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A novel classification of periodontal phenotype integrating hard and soft tissue parameters using data-driven clustering

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Abeer A. Al-Sosowa,
  • Ehab A. Abdulghani,
  • Anna Dai,
  • Jia-ping Huang,
  • Wang Meng,
  • Jiajun Zhu,
  • Pei-Hui Ding

摘要

Objective

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.

Materials and methods

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.

Results

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.

Conclusion

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.

Clinical relevance

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.