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Updated: May 9, 2026

Quantitative Assessment Protocol for Facial Soft Tissue Volumetric Changes with Stereophotogrammetry
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Published on: December 9, 2025

Three Data-Driven Lip Soft-Tissue Phenotypes and Their Dento-Skeletal Associations in Orthodontic Patients: An

Xiaoqi Zhang1, Lu Xing1, Waseem Ai-Gumaei1

  • 1State Key Laboratory of Oral Diseases, National Clinical Research Center of Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.

Orthodontics & Craniofacial Research
|May 8, 2026
PubMed
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Unsupervised machine learning identified three lip soft-tissue phenotypes, complementing traditional facial analysis. These phenotypes correlate with skeletal classes, improving craniofacial assessments.

Area of Science:

  • Orthodontics and Dentofacial Orthopedics
  • Medical Imaging Analysis
  • Machine Learning in Healthcare

Background:

  • Conventional facial diagnosis primarily focuses on skeletal and dental measurements, often neglecting detailed lip soft-tissue analysis.
  • Lip features are typically assessed in isolation, limiting a comprehensive understanding of facial morphology.
  • There is a need for data-driven approaches to classify lip soft-tissue variations.

Purpose of the Study:

  • To identify distinct lip soft-tissue phenotypes using unsupervised machine learning techniques.
  • To investigate the associations between these identified lip phenotypes and underlying skeletal and dental characteristics.
  • To develop a more holistic approach to facial analysis by integrating soft-tissue data.

Main Methods:

Keywords:
facial profilelipmachine learningorthodonticsorthognathic surgerysoft tissueunsupervised clustering

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  • A retrospective cross-sectional study utilizing 10 lip measurements from lateral cephalometric radiographs.
  • K-means clustering was employed for phenotype identification, with cluster stability validated through resampling and cross-validation.
  • Machine learning models (PCA, Random Forest, SVM, XGBoost) were used for feature importance, and a decision tree was developed for phenotype assignment.

Main Results:

  • Three reproducible lip soft-tissue phenotypes were identified: Thin-Straight, Protrusive-Convex, and Thick-Concave.
  • These phenotypes demonstrated a broad correspondence with skeletal Classes I, II, and III, respectively.
  • Lip phenotype independently predicted incisor inclination, highlighting its significance beyond skeletal factors.

Conclusions:

  • Unsupervised learning successfully identified three distinct lip soft-tissue phenotypes.
  • These phenotypes offer a valuable addition to conventional dentoskeletal descriptions by capturing soft-tissue heterogeneity.
  • Further prospective validation and outcome-based studies are necessary before clinical application.