Accurate gingival segmentation from 3D images with artificial intelligence: an animal pilot study.

Journal: Progress in orthodontics
PMID: 37121951

Abstract

BACKGROUND: Gingival phenotype plays an important role in dental diagnosis and treatment planning. Traditionally, determining the gingival phenotype is done by manual probing of the gingival soft tissues, an invasive and time-consuming procedure. This study aims to evaluate the feasibility and accuracy of an alternatively novel, non-invasive technology based on the precise 3-dimension (3D) soft tissue reconstruction from intraoral scanning and cone beam computed tomography (CBCT) to predict the gingival biotype.

Authors

  • Min Yang
    College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, Shandong, China.
  • Chenshuang Li
    Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, 240 S 40Th St., Philadelphia, PA, 19104, USA. lichens@upenn.edu.
  • Wen Yang
    Department of Pharmaceutical Analysis, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China; Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
  • Chider Chen
    Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Chun-Hsi Chung
    Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, 240 S 40Th St., Philadelphia, PA, 19104, USA.
  • Nipul Tanna
    Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, 240 S 40Th St., Philadelphia, PA, 19104, USA.
  • Zhong Zheng
    National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.

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