Evidence map›Paper›PMID 41518398›Full record

ArticleClinical oral investigations2026

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

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

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Article in Clinical oral investigations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Abeer A Al-SosowaStomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China.
Ehab A AbdulghaniState Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Anna DaiStomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China.
Jia-Ping HuangStomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China.
Wang MengStomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China.
Jiajun ZhuStomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China. zhujj@zju.edu.cn.
Pei-Hui DingStomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China. phding@zju.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 226-2024-00202Key Research and Development Program of Zhejiang Province 2022C03088Medical Interdisciplinary Innovation Program 2024 (Zhejiang University School of Medicine) nullNational Natural Science Foundation of China 82470973, 82170953Science Fund for Distinguished Young Scholars of Zhejiang Province LR23H140001
6 · The paper itself

Abstract

objectiveTo 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

methodsThis 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.

resultsCluster 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.

conclusionThis 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.

Indexed as

Alveolar ProcessGingivaAdultCluster AnalysisClustering AlgorithmsCone-Beam Computed TomographyCross-Sectional StudiesFemaleHumansMaleMiddle AgedPhenotypeAlveolar bone thicknessCBCT-intraoral scan superimpositionData-driven clusteringGingival thicknessKeratinized tissue widthPeriodontal phenotype

Identifiers

PMID41518398

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.