Machine learning for automated identification of anatomical landmarks in ultrasound periodontal imaging.

Journal: Dento maxillo facial radiology
PMID:

Abstract

OBJECTIVES: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.

Authors

  • Baiyan Qi
    Materials Science and Engineering Program, University of California San Diego, La Jolla, California, United States of America.
  • Lekshmi Sasi
    Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA 92093, United States.
  • Suhel Khan
    Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA 92093, United States.
  • Jordan Luo
    Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA 92093, United States.
  • Casey Chen
    Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA 90089, United States.
  • Keivan Rahmani
    Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA.
  • Zeinab Jahed
    Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA. zjahed@ucsd.edu.
  • Jesse V Jokerst
    Materials Science and Engineering Program, University of California San Diego, La Jolla, California, United States of America.