Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning.

Journal: International dental journal
PMID: 39875279

Abstract

INTRODUCTION AND AIM: The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators.

Authors

  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Linlin Li
    Department of Clinical Pharmacy, School of Pharmacy, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, Shandong, 271016, China.
  • Wenchong Xu
    State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
  • Yuting Guo
    State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
  • Min Xu
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Shengyuan Huang
    Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Dong Dai
    Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Chang Lu
    College of Computer and Information Science, Southwest University, Chongqing 400715, China.
  • Shuai Li
    School of Molecular Biosciences, Center for Reproductive Biology, College of Veterinary Medicine, Washington State University.
  • Jiang Lin
    Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. Electronic address: kelvinperio@163.com.

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